Miracle-dz/UniFAR-FTUs huggingface dataset:
Mid-infrared spectroscopy of SVS13: Silicates, quartz and SiC in a protoplanetary disc

We present $N$-band (8$-$13 $\mu$m) spectroscopic observations of the low-mass, embedded pre-main-sequence close binary system SVS13. Absorption features are clearly detected which are attributable to amorphous silicates, crystalline forsterite, crystalline enstatite and annealed SiO$_{2}$. Most intriguingly, a major component of the dust in the envelope or disc around SVS13 appears to be SiC, required to model adequately both the total intensity and polarisation spectra. Silicon carbide is a species previously detected only in the spectra of C-rich evolved star atmospheres, wherein it is a dust condensate. It has not been unambiguously identified in the interstellar medium, and never before in a molecular cloud, let alone in close proximity to a forming star. Yet pre-Solar grains of SiC have been identified in meteorites, possibly suggesting an interesting parallel between SVS13 and our own Solar-System evolution. The uniqueness of the spectrum suggests that we are either catching SVS13 in a short-lived evolutionary phase and/or that there is something special about SVS13 itself that makes it rare amongst young stars. We speculate on the physical origin of the respective dust species and why they are all simultaneously present toward SVS13. Two scenarios are presented: a disc-instability-induced fragmentation, with subsequent localised heating and orbital evolution firstly annealing initially amorphous silicates and then dispersing their crystalline products throughout a circumstellar disc; and a newly discovered shock-heating mechanism at the interface between the circumstellar and circumbinary discs providing the crystallisation process. One or both of these mechanisms acting on carbon-rich grain material can also feasibly produce the SiC signature.
Genetic Analysis of Agronomic and Quality Traits from Multi-Location white Yam Trials using Mixed Model with Genomic Relationship Matrix

Traits that define the suitability of a crop for production and consumption are often assessed and predicted to identify superior genotypes for commercial deployment. This study assessed genetic parameter estimates and prediction for 25 agronomic and quality traits in 49 white yam clones. It employed best linear unbiased prediction (BLUP) in a mixed model analysis using genomic relationship matrix derived from 6337 Diversity Array Technology (DArT) molecular markers, multivariate technique of the principal component and canonical discriminant analysis with BLUP predicted values to select key traits for yam breeding. Findings revealed that additive genetic, non-additive genetic and non-genetic factors contributed substantially to phenotypic variation of the studied yam traits. The non-genetic effects accounted for higher variation than the total genetic effects for majority of the traits except yam mosaic virus (YMV), tuber number per plant, ash content, flour yield, peel loss, and protein content. The narrow sense heritability was generally low (<0.30) for all traits except yam anthracnose (0.31), ash content (0.30) and peel loss (0.89). Trait selection with multivariate analysis identified 15 from the 25 traits with fresh tuber yield, tuber dry matter content (DMC), YMV, root-knot and Scutellonema bradys nematode susceptibility as the most important traits for white yam variety testing. This paper presents the importance of complementing BLUP prediction that accounts for the relationship among the genotypes with multivariate analysis for genetic parameter estimation, prediction and selection in yam breeding trials to accelerate the genetic gains.
Cosmological limit on the neutrino mass

We have performed a careful analysis of constraints on the neutrino mass from current cosmological data. Combining data from the cosmic microwave background and the 2dF galaxy survey yields an upper limit on the sum of the three neutrino mass eigenstates of \sum m_nu < 3 eV (95% conf.), without including additional priors. Including data from SNIa observations, Big Bang nucleosynthesis, and HST Hubble key project data on H_0 tightens the limit to \sum m_nu < 2.5 eV (95% conf.). We also perform a Fisher matrix analysis which illustrates the cosmological parameter degeneracies affecting the determination of \sum m_nu.
The genetic diversity and variation in crude protein content of wheat (Triticum aestivum L.) promising cultivars for breeding in Albania

The genetic diversity and variation in crude protein content among eleven wheat genotypes, comprising three elite local genotypes and eight wheat genotypes of foreign origin were investigated in the present study. Variability was evidenced in grain protein content estimated by biuret test, it ranged from 9.5 to 13.9% with mean of 11.58%. Comparative analysis between local and introduced wheat genotypes revealed that the local genotypes had lower protein content than those of foreign origin. Fourteen polymorphic RAPD markers were used to assess genetic diversity among selected wheat varieties. The mean similarity among wheat genotypes was 67%. Genetic similarity among local wheat varieties was higher (83%) than among those of foreign origin (66%). The wheat genotypes were grouped into two main clusters on UPGMA dendrogram constructed based on Dice similarity coefficients. A clear clustering of genotypes according to the origin wasobserved. This clustering was also supported by principal coordinate analysis (PCoA) results. There was no observed clustering based on the protein content. The data revealed that local wheat genetic had narrow genetic diversity, however the wheat genotypes of foreign origin constitute a promising material to be employed in breeding programs aiming the increase of wheat protein content and genetic diversity.
Educational Sequence Mining for Dropout Prediction in MOOCs: Model Building, Evaluation, and Benchmarking

Due to the unprecedented growth in available data collected by e-learning platforms, including platforms used by massive open online course (MOOC) providers, important opportunities arise to structurally use these data for decision making and improvement of the educational offering. Student retention is a strategic task that can be supported by means of automated data-driven dropout prediction. Given the time-based nature of the collected data (user activity), these data can be viewed as sequences, and thus, sequence mining presents itself as a fitting set of techniques to automatically extract valuable insights. However, there is a lack of general guidelines for using sequence mining in specific educational settings, as well as little information on how different techniques perform in comparison to each other. We address these limitations with two main contributions. First, we propose a framework for applying sequence classification for dropout prediction in MOOCs. This framework includes two data-driven dropout definitions, the specification of data formatting and preparation tasks, and a blackprint on how to train dropout prediction models at suitable time points in the run of the course. Second, we conduct a benchmarking study of recent and well-performing sequence classification techniques, tested with different parametrizations on 47 real-life datasets from MOOCs, resulting in a comparative assessment of over 18 000 models. Our results provide insight into the performance differences between the techniques and allow us to formulate concrete recommendations toward the choice of suitable hyperparameters that have a significant influence on the predictive performance.
Value Bounds and Best Response Violations in Discriminatory Share Auctions

This paper analyzes a discriminatory share auction in which bidders submit non-increasing step functions with a bounded number of steps, the type space consists of private non-increasing marginal valuation functions, and the number of participants is random. I show that the interim utility can be written as a simple functional of the distribution of the allocated quantity. This allows me to derive equilibrium existence and to give a characterization of the equilibrium bid schedules in terms of the individual bidders' optimality conditions. The characterization facilitates the formulation of bounds on the estimates of marginal valuations between the submitted quantity points and permits a simple estimator of the fraction of best response violations among the submitted bids. Proofs of concept for the bounds and the estimator are given by using a novel data set from meat import quota auctions in Switzerland.
Joint EEG Feature Transfer and Semisupervised Cross-Subject Emotion Recognition

Due to the weak and nonstationary properties, electroencephalogram (EEG) data present significant individual differences. To align data distributions of different subjects, transfer learning showed promising performance in cross-subject EEG emotion recognition. However, most of the existing models sequentially learned the domain-invariant features and estimated the target domain label information. Such a two-stage strategy breaks the inner connections of both processes, inevitably causing the suboptimality. In this article, we propose a joint EEG feature transfer and semisupervised cross-subject emotion recognition model in which the shared subspace projection matrix and target label are jointly optimized toward the optimum. Extensive experiments are conducted on SEED-IV and SEED, and the results show that the emotion recognition performance is significantly enhanced by the joint learning mode and the spatial-frequency activation patterns of critical EEG frequency bands and brain regions in cross-subject emotion expression are quantitatively identified by analyzing the learned shared subspace.
Measuring motion through relativistic quantum effects

We show that the relativistic signatures on the transition probability of atoms moving through optical cavities are very sensitive to their spatial trajectory. This allows for the use of internal atomic degrees of freedom to measure small time-dependent perturbations in the proper acceleration of an atomic probe, or in the relative alignment of a beam of atoms and a cavity.
Dwarf Galaxies and the Origin of the Intracluster Medium

Following the recent suggestion that it is dwarf galaxies in clusters -- as opposed to large ellipticals -- that provide the intracluster gas, we estimate the metallicity of the intracluster medium (ICM) in such a case. We derive analytical expressions for the fraction of mass of dwarf galaxies that is ejected, and estimate the metallicity of the resulting intracluster gas. We find that the metallicity resulting from this hypothesis is adequate only for clusters with low-metallicity gas. Since galactic winds from dwarf galaxies occur much earlier than those from ellipticals, due to the smaller binding energy of the gas, we show that the gas ejected by dwarf galaxies is enriched mostly by Type II supernovae, as opposed to Type I supernovae in the case of large galaxies. We further point out that the gas in small scale structures, which never cools and forms stars due to low temperatures and, consequently, large cooling time scale, gets dispersed in the process of hierarchical clustering, and is incorporated as the diffuse intracluster gas. We show that this process can provide enough hot gas to be compatible with X-ray observations in rich clusters.
Two and three photon excited luminescence of single gold nanoparticles: Switching between plasmon- and electron-hole-pair emission by ultrashort laser pulses.

In this work we use femtosecond laser pulses of 800 nm wavelength to excite and characterize the multiphoton luminescence emission of single gold nanoparticles. For excitation with 100 fs laser pulses we observe a two and three photon emission dominated by radiative electron hole pair recombination, while the emission is caused by radiative plasmon decay for excitation with 500 fs pulses. For single gold nanorods with different aspect ratios, we study the interplay between the particle plasmon and electron hole pairs, which enables us to develop a quantitative model to fully describe the two and three photon luminescence emission of single gold nanoparticles.
Datamorphic Testing: A Method for Testing Intelligent Applications

Adequate testing of AI applications is essential to ensure their quality. However, it is often prohibitively difficult to generate realistic test cases or to check software correctness. This paper proposes a new method called datamorphic testing, which consists of three components: a set of seed test cases, a set of datamorphisms for transforming test cases, and a set of metamorphisms for checking test results. With an example of face recognition application, the paper demonstrates how to develop datamorphic test frameworks, and illustrates how to perform testing in various strategies, and validates the approach using an experiment with four real industrial applications of face recognition.
Anaerobic rumen fungi and fungal direct-fed microbials
in ruminant feeding

The article discusses the importance and role of fungal direct-fed microbials (DFM) and anaerobic rumen fungi (ARF) in ruminants and their metabolic pathways of plant fibre decomposition, as well as rumen fermentation processes. ARF classification, enzymatic activity, impact on rumen metabolism, as well as fungal DFM and application of their metabolites in ruminant feeding are presented. The research area concerning ARF in ruminant feeding is gaining interest as the subject is still poorly elucidated. The latest research in the field of ruminant physiology and nutrition has pointed to a significant impact of ARF and fungal DFM on nutrient degradability, fermentation profile in the rumen, and animal performance. Although, the proportion of fungi in the total rumen microorganisms is low (approximately 8% of rumen biomass), they play a crucial role in generating a series of enzymes utilising difficult-to-debase compounds, such as cellulose, hemicelluloses and xylose. Although enzymes secreted by ARF are not able to degrade lignin, they can solubilise lignocellulosic complexes and expand the surface area for enzymatic activity. This leads to a better utilisation of fibrous feeds, increased nutrient digestibility, and enhanced rumen fermentation. A comprehensive description and discussion of these issues in the review provides an in-depth look at the role of ARF and the potential use of ARF as DFM in the ruminant nutrition in a-broad perspective.
PHYSICAL PROPERTIES OF MOLECULAR CLOUDS FOR THE ENTIRE MILKY WAY DISK

This study presents a catalog of 8107 molecular clouds that covers the entire Galactic plane and includes 98% of the 12CO emission observed within . The catalog was produced using a hierarchical cluster identification method applied to the result of a Gaussian decomposition of the Dame et al. data. The total H2 mass in the catalog is , in agreement with previous estimates. We find that 30% of the sight lines intersect only a single cloud, with another 25% intersecting only two clouds. The most probable cloud size is pc. We find that , with no correlation between the cloud surface density, Σ, and R. In contrast with the general idea, we find a rather large range of values of Σ, from 2 to 300 M⊙ pc−2, and a systematic decrease with increasing Galactic radius, . The cloud velocity dispersion and the normalization both decrease systematically with . When studied over the whole Galactic disk, there is a large dispersion in the line width–size relation and a significantly better correlation between and . The normalization of this correlation is constant to better than a factor of two for . This relation is used to disentangle the ambiguity between near and far kinematic distances. We report a strong variation of the turbulent energy injection rate. In the outer Galaxy it may be maintained by accretion through the disk and/or onto the clouds, but neither source can drive the 100 times higher cloud-averaged injection rate in the inner Galaxy.
Predicting Stable Stoichiometries of Compounds via Evolutionary Global Space-Group Optimization

Whereas the Daltonian atom-to-atom ratios in ordinary molecules are well understood via the traditional theory of valence, the naturally occurring stoichiometries in intermetallic compounds ApBq, as revealed by phase-diagram compilations, are often surprising. Even equal-valence elements A and B give rise to unequal p ,q stoichiometries, e.g., the 1:2, 2:1, and 3:1 ratios in AlpScq. Moreover, sometimes different stoichiometries are associated with different lattice types and hence rather different physical properties. Here, we extend the fixed-composition global space-group optimization GSGO approach used to predict, via densityfunctional calculations, fixed-composition lattice types G. Trimarchi and A. Zunger, J. Phys.: Condens. Matter 20, 295212 2008 to identify simultaneously all the minimum-energy lattice types throughout the composition range. Starting from randomly selected lattice vectors, atomic positions and stoichiometries, we construct the T=0 “convex hull” of energy vs composition. Rather than repeat a set of GSGO searches over a fixed list of stoichiometries, we minimize the distance to the convex hull. This approach is far more efficient than the former one as a single evolutionary search sequence simultaneously identifies the lowest-energy structures at each composition and among these it selects those that are ground states. For Al-Sc we correctly identify the stable stoichiometries and relative structure types: AlSc2-B82, AlSc-B2, and Al2Sc-C15 in the Nat=6 periodic cells, and Al2Sc6-D019, AlSc-B2, and Al3Sc-L10 in the Nat=8 periodic cells. This extended evolutionary GSGO algorithm represents a step toward a fully ab initio materials synthesis, where compounds are predicted starting from sole knowledge of the chemical species of the constituents.
Spherical Random Arrays With Application to Aerial Collaborative Beamforming

Spherical volumetric arrays with randomly distributed elements are studied using geometrical probability theory. The array elements are considered as points of an isotropic homogeneous binomial point process (BPP), randomly located within a 3-D ball. The presented results are based on the distance distribution of the $k$ th nearest element from the center of the ball. These elements are assumed to be the randomly distributed nodes of a wireless aerial network that cooperate to produce a beam toward a desired distant receiver. An alternative array elements’ distribution is also examined by placing a constraint to the distance of the farthest element and providing novel results on the average array factor, the average power pattern, and the directivity of the array. The half power beamwidth (HPBW), the peak sidelobe level (SLL), and the location of the peaks and nulls of the power pattern are examined in terms of the number of the array elements and the radius of the sphere. Finally, the impact of the location estimation errors on the performance of open-loop collaborative beamforming is examined both theoretically and using empirical simulation results.
Fairtrade as a Social Innovation: Brazilian Experience of Rural Organisations

The objective of this article is to describe the dynamics of fair trade in organisations that adhered to the Fair Trade International system, based on the principles of fair trade and the dimensions of social innovation, and to identify why there was a disruption in commercial transactions. Four organisations from the state of Rio Grande do Norte, Brazil, which won the fair trade seal, were analysed. Primary data collection occurred through semi-structured interviews and simultaneous annotations and, later, secondary data were collected from official documents, articles, academic works and websites. The results show that the experiences of organisations with Fair Trade International have numerous properties of the dimensions of social innovation. There was the emergence of an emerging development model with the opportunity to work collaboratively and collectively. These changes required adaptation and new practices, as producers underwent a period of changes in their working and production. The formation of cooperatives and fair trade certification had the mobilisation and participation of various actors, who also provided collective learning, both in technical terms and in terms of civic and interpersonal management, especially for rural producers.
A New Self-Powered Temperature Sensor Based on Thermoelectric Generators

This article presents a new self-powered temperature sensor based on thermoelectric generators (TEGs). Two different types of TEGs measure temperature and supply the interface circuit. The interface circuit calculates and generates an output current proportional to the cold-surface temperature of the TEGs. Since TEG output voltage depends on the temperature difference between the cold and hot surfaces, a three-terminal TEG has been used. A combination structure of the two PbTe and Bi2Te3 TEGs are thermally in parallel and electrically in series. The new method uses a machine-learning algorithm and Seebeck curve fitting for temperature calculation. For the cold surface temperature range of 0 °C to 70 °C, the error is less than ±1 °C.
Historical and projected climatic trends in KwaZulu-Natal: 1950–2100

The climate of KwaZulu-Natal, South Africa, is evaluated for historical and projected trends in the period 1950–2100. This region lies next to the warm Indian Ocean and experiences an alternating airflow imposed by subtropical easterly and mid-latitude westerly wind belts. Multi-year wet spells have diminished since 2001 and potential evaporation deficits have spread from the Tugela Valley. Although coastal vegetation is greening and sea temperatures in the Agulhas Current are warming (>0.02‧yr−1), there are fewer rain days and less cloud cover. Tropical winds across southern Africa have turned toward Madagascar, re-directing moisture and convection away from KwaZulu-Natal in recent decades. Long-range coupled model projections of monthly rainfall display weak trends over the 21st century (−0.01 mm‧day−1‧yr−1) which are overshadowed by multi-year fluctuations (r2 = 0.04). In contrast, drying trends in potential evaporation are significant (r2 = 0.41). Forecasts of seasonal dry spells could mitigate climate change impacts in south-eastern Africa.
Deep Architecture based on DenseNet-121 Model for Weather Image Recognition

—Weather conditions have a significant effect on humans’ daily lives and production, ranging from clothing choices to travel, outdoor sports, and solar energy systems. Recent advances in computer vision based on deep learning methods have shown notable progress in both scene awareness and image processing problems. These results have highlighted network depth as a critical factor, as deeper networks achieve better outcomes. This paper proposes a deep learning model based on DenseNet-121 to effectively recognize weather conditions from images. DenseNet performs significantly better than previous models; it also uses less processing power and memory to further increase its efficiency. Since this field currently lacks adequate labeled images for training in weather image recognition, transfer learning and data augmentation techniques were applied. Using the ImageNet dataset, these techniques fine-tuned pre-trained models to speed up training and achieve better end results. Because DenseNet-121 requires sufficient data and is architecturally complex, the expansion of data via geometric augmentation—such as rotation, translation, flipping, and scaling—was critical in decreasing overfitting and increasing the effectiveness of fine-tuning. These experiments were conducted on the RFS dataset, and the results demonstrate both the efficiency and advantages of the proposed method, which achieved an accuracy rate of 95.9%.