Miracle-dz/UniFAR-FTUs huggingface dataset:
APERTURE SYNTHESIS OBSERVATIONS OF CO, HCN, AND 89 GHz CONTINUUM EMISSION TOWARD NGC 604 IN M33: SEQUENTIAL STAR FORMATION INDUCED BY A SUPERGIANT H ii REGION

We present the results from new Nobeyama Millimeter Array observations of CO(1–0), HCN(1–0), and 89 GHz continuum emission toward NGC 604, known as the supergiant H ii region in the nearby galaxy M33. Our high spatial resolution images (4.″2 × 2.″6, corresponding to 17 pc × 11 pc physical size) of CO emission allowed us to uncover 10 individual molecular clouds that have masses of (0.8–7.4) ×105 M☉ and sizes of 5–29 pc, comparable to those of typical Galactic giant molecular clouds. Moreover, we detected for the first time HCN emission in the two most massive clouds and 89 GHz continuum emission at the rims of the “Hα shells. ” The HCN and 89 GHz continuum emission are offset from the CO peak and are distributed in the direction of the central cluster. Three out of ten CO clouds are well correlated with the Hα shells both in spatial and velocity domains, implying an interaction between molecular gas and the expanding H ii region. The CO clouds show varieties in star formation efficiencies (SFEs), which are estimated from the 89 GHz emission and combination of Hα and Spitzer 24 μm data. Furthermore, we found that the SFEs decrease with increasing projected distance measured from the heart of the central OB star cluster in NGC 604, suggesting radial changes in the evolutionary stages of the molecular clouds in the course of stellar cluster formation. Our results provide further support to the picture of sequential star formation in NGC 604 initially proposed by Tosaki et al. with the higher spatially resolved molecular clouds, in which an isotropic expansion of the H ii region pushes gases outward, which accumulates to form dense molecular clouds, and then induces massive star formations.
Effect of monocalcium phosphate on the concentration of cadmium chemical fractions in two calcareous soils in Iran

This study has been done to see the impact of phosphorus (P) on the concentration of cadmium (Cd) chemical fractions in two sorts of calcareous soils within the Fars province in Iran. Because of these interactions, we looked at the in ﬂ uence of phosphorus on cadmium fractions. Variables were three levels of Cd (0.0, 30.0 and 60.0 mg kg -1 of soil from CdSO 4. 8H 2 O), three levels of p (0.0, 50.0 and 100.0 mg kg -1 of soil from Ca (H 2 PO 4 ) 2. H 2 O, three levels Incubation time (2, 4 and 8 weeks) and two sorts of soil (clay and sandy clay loam). The randomized completed block design (RCBD) was used for this research. After 2, 4 and 8 weeks of treatments, the sequential extraction procedure was done to determine cadmium concentration in WsEx (Water Soluble and Exchangeable), Sorb (EDTA ex-tractable), MnOx (Manganese Oxides), Car (Carbonate), OM (Organic matter), AFeOx (Amorphous Iron Oxides), CFeOx (Crystaline Fe oxides) and Res (Residual) fractions. The results showed that 69.0 to 71.0% of the added Cd was removed within the WsEx, Sorb and MnOx fractions. Cadmium concentration in Sorb, OM and Res fraction was higher within clay soil while cadmium concentration within WsEx and Car fractions were higher within sandy clay loam soil. Adding P as monocalcium phosphate reduced cadmium concentration within WsEx and Sorb fractions while increased Car, OM and Res fractions. The presence of phosphorus reduces the concentration of Cd in those forms that are easily released into soil solution (WsEx and Sorb) from where they’ll be absorbed by plants and thus decrease cadmium uptake by plants.
Authentic Transformational Leadership and Empowerment in Organization Context - A Review of Literature

: Whether in the government organizations, private sector, or not-for-profits, barely a day ends without the media highlighting on unethical, inauthentic conduct of a leader. Within the leadership literature, not much attention has been paid to Authentic transformational leadership. And the limited literature available covers very few contexts. The purpose of this conceptual paper is to explore how authentic transformational leadership could lead to authentic transformational leadership and the empowerment of individuals, organizations, and communities at large. The paper analyses existing conceptual, theoretical, and empirical studies and raise a number of immerging issues that are used to present a new theoretical model appropriate for the development and application of the Authentic transformational leadership construct and its resulting outcomes, specifically, empowerment. Three theories that have informed the study are transformative paradigm, empowerment theory, and participatory theory. The paper further recommends an integrated theoretical framework for exploring Authentic Transformational Leadership and enhancing empowerment while recognizing the significant role played by religion, credible and cooperative environment in the context of the twenty-first-century organization. situational leadership, path-goal leadership, the 'Leader-member exchange (LMX) theory, transformational leadership, authentic leadership, servant Leadership, adaptive leadership focuses and team leadership.
Current-voltage (I-V) characteristics of armchair graphene nanoribbons under uniaxial strain

The current-voltage characteristics of armchair graphene nanoribbons under a local uniaxial tension are investigated by using first-principles quantum transport calculations. It is shown that for a given value of bias voltage, the resulting current depends strongly on the applied tension. The observed trends are explained by means of changes in the band gaps of the nanoribbons due to the applied uniaxial tension. In the course of plastic deformation, the irreversible structural changes and derivation of carbon monatomic chains from graphene pieces can be monitored by two-probe transport measurements.
Mutual learning graph convolutional network for hyperspectral image classification

Graph convolutional networks (GCNs) have achieved great success in hyperspectral image (HSI) classification. However, there are also some difficulties that need to be solved, such as the lack of enough labeled samples in the training process and the difference between the numbers of different land-cover analogies is too large, which will lead to poor classification performance. In order to alleviate those problems, we propose a Mutual Learning Graph Convolutional Network (called MLGCN) with an imbalance loss, which trains two GCN simultaneously, and these two GCNs can learn from each other by using mutual learning strategy in the training process. Experiments on three real data sets show that the proposed MLGCN method achieves state-of-the-art results in terms of three classification evaluation metrics, including overall accuracy (OA), average accuracy (AA), kappa coefficient (κ),.
Novel pH-Responsive Cubosome and Hexosome Lipid Nanocarriers of SN-38 Are Prospective for Cancer Therapy

Highlights1) What are the main findings? Lipid nanoparticles containing novel synthetic aminolipids were formulated. Their internal nanostructures are sensitive to pH, being an inverse hexagonal phase at pH 7.4 and a bicontinuous cubic phase at pH 4.0–pH 7.0. Poorly soluble and highly potent anticancer drug SN-38 was encapsulated successfully in the nanoparticles with a ~100-fold increase in solubility. SN-38 was released faster at a tumour-relevant acidic pH compared to neutral pH. 2) What is the implication of the main finding? Aminolipids can be incorporated into self assembled lipid nanoparticles to achieve desirable pH responsiveness. The SN-38-loaded pH-sensitive lipid nanoparticles represent a promising candidate for delivering the potent drug SN-38 to tumour sites. The pH-sensitive lipid nanoparticles in this study can be a viable alternative to the currently used irinotecan. Abstract pH-responsive nanoparticles enable the selective delivery of a chemotherapeutic agent to tumours while reducing adverse effects. Herein we synthesised four novel aminolipids and developed pH-responsive nanostructured lipid nanoparticles (LNP), which exhibited a slow-releasing hexagonal structure (H2) at physiological pH and quick release bicontinuous cubic phase (Q2) at the acidic tumour pH. The nanoparticles were used to encapsulate and control the release of the chemotherapeutic agent SN-38. High-throughput formulation techniques were employed to fabricate LNP by mixing various amounts of aminolipid with monoolein (MO). The effect of aminolipids on MO self-assembled structures was studied using small-angle X-ray scattering (SAXS) at various pH values. Out of the four studied aminolipid-MO LNP systems, the nanoparticles containing N-(Pyridin-4-ylmethyl) oleamide (OAPy-4) or N-(2(piperidin-1yl)ethyl) oleamide (OAPi-1) exhibited a pH-induced H2 to Q2 phase transition in a tumour-relevant pH range (pH 5.5–7.0). SN-38 is 1000 times more efficacious than the commercially available prodrug irinotecan. However, low solubility in water and instability at physiological pH makes it unsuitable for clinical use. SN-38 was loaded into LNP containing MO and aminolipid OAPy-4. The drug loading and entrapment efficiency were determined, and the results indicated that the aqueous solubility of SN-38 loaded in LNP dispersions was ~100 times higher compared to the solubility of the pure drug in aqueous solution. Furthermore, we demonstrated that the in vitro SN-38 release rate from LNPs was faster at lower pH (pH 5) than at neutral pH. Therefore, pH-responsive LNPs developed in this study can potentially be employed in delivering and controlling the release of the potent drug SN-38 to tumour sites.
Link prediction based on collaborative filtering

The research of link prediction is widely used in many fields such as social relations and biological sciences. So it has become a very important research direction. Existing link prediction methods are usually based on the assumption that "the more similar two nodes are, the higher the probability that a link exists between them". That is, the similarity between nodes is considered as the existence probability of a link between nodes. Such an approach does not fully exploit the hidden information in the network, so the accuracy of prediction needs to be further improved. In this paper, we propose a link prediction model based on collaborative filtering. The model combines the similarities between nodes and the known network topology by the method of collaborative filtering, which effectively improves the accuracy of link prediction. Extensive experiments on six real-world datasets demonstrate the validity of the proposed model.
Automatic wildfire monitoring system based on deep learning

ABSTRACT Fire detection based on computer vision technology can avoid many flaws in conventional methods. However, existing methods fail to achieve a good trade-off in accuracy, model size, speed, and cost. This paper presents a high-performance forest fire recognition algorithm to solve the current problems in forest fire monitoring. Firstly, visual saliency areas in motion images are extracted to improve detection efficiency. Secondly, transfer learning techniques are employed to improve the generalization performance of the constructed deep learning classification model. Finally, fire detection is realized based on C++ deployment algorithms Compared with the existing forest fire detection methods, the proposed method has higher classification accuracy and speed, with a more comprehensive application range and lower cost. The performance of our method can meet the accuracy and speed requirements of real-time fire detection, and it can be deployed and practiced on multiple platforms.
Crystallization in three dimensions: Defect-driven topological ordering and the role of geometrical frustration

Herein, fundamentals of topology and symmetry breaking are used to understand crystallization and geometrical frustration in topologically close-packed structures. This frames solidification from a new perspective that is unique from thermodynamic discussions. Crystallization is considered as developing from undercooled liquids, in which orientational order is characterized by a surface of a sphere in four-dimensions (quaternion) with the binary polyhedral representation of the preferred orientational order of atomic clustering inscribed on its surface. As a consequence of the dimensionality of the quaternion orientational order parameter, crystallization is seen as occurring in ``restricted dimensions. '' Homotopy theory is used to classify all topologically stable defects, and third homotopy group defect elements are considered to be generalized vortices that are available in superfluid ordered systems. This topological perspective approaches the liquid-to-crystalline solid transition in three-dimensions from fundamental concepts of: Bose-Einstein condensation, the Mermin-Wagner theorem and Berezinskii-Kosterlitz-Thouless (BKT) topological-ordering transitions. In doing so, in this article, concepts that apply to superfluidity in ``restricted dimensions'' are generalized to consider the solidification of crystalline solid states.
Population dynamics and biological aspects of Tetranychus urticae on three squash (Cucurbita pepo) varieties

Squash (Cucurbita pepo L.) is a popular plant in Egypt, field study was carried out to evaluate the susceptibility of three squash varieties, i.e. Eskandarani, Hytech and Miletto infestation with two spotted spider mite Tetranychus urticae. Temperature and humidity played a strong role in the T. urticae infestation on squash varieties where there is a positive relationship between the population of T. urticae and temperature while negative between the population and humidity. Also, temperature plays an important role on biology of T. urticae where they completed their development and reproduced at all tested temperatures (20, 25, 30ÂºC). Also developmental time, oviposition period, adult longevity and life span decreased with increasing temperatures, while daily oviposition rate and fecundity increased with increasing temperatures. Therefore 30 ºC is the ideal degree in which all stages of growth are shortened. On the other hand the susceptibility of squash varieties to the T. urticae infestation attributable to phytochemical contents which varied from variety to another, and affect mite density levels. A positive relationship was found between mite infestations and squash leaf contents i. e. total carbohydrates, total protein and total lipids in squash leaves, while a negative one found with alkaloids and total phenolic compounds, total flavonoids and total carotenoids and chlorophyll (a&b). Thus temperature and humidity and phytochemical contents of plant leaves has an important effect on infesting squash by T. urticae.
Selective exposure: Exposing a Few Selected Theoretical Aspects

Selective exposure is a phenomenon studied by scholars for decades. Its prominence can be explained by certain potential consequences for democratic societies which include polarization and growing support for extreme views.The media selective exposure approach generated hundreds of publications, however, this growth in new facts and information does not seem to advance much of a paradigmatic consensus on theoretical foundations and practical utility of this line of research.This article aims at assessing whether the key concepts and models of selective exposure represent a cohesive body of knowledge empowering researchers. It also encourages them to seek new knowledge, and test new links. Researchers can also evaluate whether there are some controversial or not sufficiently explicated elements requiring reassessment.This article is a modest effort to assess what is really known and agreed upon in such important pillars of any theory such as definitions and models of selective exposure. This piece also suggests which aspects of selective exposure may need further clarification.
The Verbal and Non Verbal Signals of Depression - Combining Acoustics, Text and Visuals for Estimating Depression Level

Depression is a serious medical condition that is suffered by a large number of people around the world. It significantly affects the way one feels, causing a persistent lowering of mood. In this paper, we propose a novel attention-based deep neural network which facilitates the fusion of various modalities. We use this network to regress the depression level. Acoustic, text and visual modalities have been used to train our proposed network. Various experiments have been carried out on the benchmark dataset, namely, Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we empirically justify that the fusion of all three modalities helps in giving the most accurate estimation of depression level. Our proposed approach outperforms the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on mean absolute error (MAE).
EFFECTS OF RESISTIVITY ON MAGNETIZED CORE-COLLAPSE SUPERNOVAE

We studied the role of turbulent resistivity in the core-collapse of a strongly magnetized massive star, carrying out two-dimensional resistive-MHD simulations. Three cases with different initial strengths of magnetic field and rotation are investigated: (1) a strongly magnetized rotating core, (2) a moderately magnetized rotating core, and (3) a very strongly magnetized non-rotating core. In each case, one ideal-MHD model and two resistive-MHD models are computed. As a result of these computations, each model shows an eruption of matter assisted by magnetic acceleration (and also by centrifugal acceleration in the rotating cases). We found that resistivity attenuates the explosion in cases 1 and 2, while it enhances the explosion in case 3. We also found that in the rotating cases, the main mechanisms for the amplification of a magnetic field in the post-bounce phase are an outward advection of the magnetic field and a twisting of poloidal magnetic field lines by differential rotation, which are somewhat dampened down with the presence of resistivity. Although magnetorotational instability seems to occur in the rotating models, it plays only a minor role in magnetic field amplification. Another impact of resistivity is that on the aspect ratio. In the rotating cases, a large aspect ratio of the ejected matter, >2.5, attained in an ideal-MHD model is reduced to some extent in a resistive model. These results indicate that resistivity possibly plays an important role in the dynamics of strongly magnetized supernovae.
AMGNET: multi-scale graph neural networks for flow field prediction

Solving partial differential equations of complex physical systems is a computationally expensive task, especially in Computational Fluid Dynamics(CFD). This drives the application of deep learning methods in solving physical systems. There exist a few deep learning models that are very successful in predicting flow fields of complex physical models, yet most of these still exhibit large errors compared to simulation. Here we introduce AMGNET, a multi-scale graph neural network model based on Encoder-Process-Decoder structure for flow field prediction. Our model employs message passing of graph neural networks at different mesh graph scales. Our method has significantly lower prediction errors than the GCN baseline on several complex fluid prediction tasks, such as airfoil flow and cylinder flow. Our results show that multi-scale representation learning at the graph level is more effective in improving the prediction accuracy of flow field.
THE AGES OF HIGH-MASS X-RAY BINARIES IN NGC 2403 AND NGC 300

We have examined resolved stellar photometry from HST imaging surrounding 18 high-mass X-ray binary (HMXB) candidates in NGC 300 and NGC 2403 as determined from combined Chandra/HST analysis. We have fit the color–magnitude distribution of the surrounding stars with stellar evolution models. All but one region in NGC 300 and two in NGC 2403 contain a population with an age between 20 and 70 Myr. One of the candidates is the ultraluminous X-ray source in NGC 2403, which we associate with a 60 ± 5 Myr old population. These age distributions provide additional evidence that 16 of these 18 candidates are HMXBs. Furthermore, our results suggest that the most common HMXB age in these galaxies is 40–55 Myr. This preferred age is similar to observations of HMXBs in the Small Magellanic Cloud, providing new evidence of this formation timescale, but in higher metallicity populations. We suggest that this preferred HMXB age is the result of the fortuitous combination of two physical effects. First, this is the age of a population when the greatest rate of core-collapse events should be occurring, maximizing neutron star production. Second, this is the age when B stars are most likely to be actively losing mass. We also discuss our results in the context of HMXB feedback in galaxies, confirming HMXBs as a potentially important source of energy for the interstellar medium in low-mass galaxies.
Analytical Approaches for Effects of Edge-Located EBG Structure With Defected Ground Structure on Cavity-Mode Resonances of Power/Ground Planes

The effects of an edge-located electromagnetic bandgap (EL-EBG) structure including a defected ground structure (DGS) on the cavity-mode resonances of power/ground planes are analyzed for the power integrity design of high-speed packages and printed circuit boards (PCBs). The EBG structure localized in the power/ground planes significantly affects the power integrity characteristics. However, previous studies did not consider this effect. An analytical equation and a numerical method based on the contour integral method (CIM) are developed to examine the EL-EBG effect on cavity-mode resonance. The proposed method reveals that the employment of the EL-EBG structure results in resonance frequency reduction, generation of hybrid-type mode, irregular relationship between mode resonance frequencies, and different distribution of mode intensity compared to conventional open-ended power/ground planes. The proposed CIM modeling and analysis for investigating the distinctive features of the EL-EBG effect are validated using full-wave simulations and measurements. The rigorous analysis of the EL-EBG effect enables the improvement of power integrity design for power/ground planes combined with EBG structures. Moreover, the proposed CIM modeling can obtain fast and accurate results for EL-EBG power/ground planes compared to full-wave simulations.
Greedy Search Method for Separable Nonlinear Models Using Stage Aitken Gradient Descent and Least Squares Algorithms

Aitken gradient descent (AGD) algorithm takes some advantages over the standard gradient descent and Newton methods: 1) can achieve at least quadratic convergence in general; 2) does not require the Hessian matrix inversion; 3) has less computational efforts. When using the AGD method for a considered model, the iterative function should be unchanging during all the iterations. This article proposes a hierarchical AGD algorithm for separable nonlinear models based on stage greedy method. The linear parameters are estimated using the least squares algorithm, and the nonlinear parameters are updated based on the AGD algorithm. Since the iterative function is changing at each iteration, a stage AGD algorithm is introduced. The convergence properties and simulation examples show effectiveness of the proposed algorithm.