Miracle-dz/UniFAR-FTUs huggingface dataset:
A study on efficiency of semi-implicit, density-based solver for simulation of evaporating particle-laden flow

In this work, we propose a semi-implicit, density-based solver for compressible, evaporating particle-laden flow, and investigate its efficiency. It is established on a Cartesian-grid-based, scalable, numerical framework named CUBE. In this solver, the governing equation system is divided into three subsystems (compressible Navier– Stokes, species transport, and Lagrangian), and these subsystems are weakly coupled in two ways. In the Lagrangian domain, the fuel spray is treated as a set of discrete particles, and the particle-source-in-cell (PSICell) method is employed for the coupling between the Eulerian and Lagrangian domains. Furthermore, the species transport and Lagrangian subsystems are subcycled with smaller time step, and the Navier–Stokes equation is temporally integrated with a larger step size. The proposed solver's verification and evaluation is conducted on the supercomputer Fugaku by comparing the results with those of the original, fully explicit solver where all equations have the same time step. The results show that this solver reduces the computational cost while ensuring similar accuracy. The solution of the proposed solver is consistent with that of the original solver. Finally, we brief our perspective on the future application of the proposed solver to our target problem: the largescale simulation of evaporating particle-laden flow in a combustor of an aviation engine.
Activity of transition metals in N‐doped carbon Electrocatalysts for hydrogen evolution reaction: Insight from quantum chemistry computations

Owning to growing trends in global environmental and energy concerns, great efforts have been carried out to find environmentally friendly sources of energy. Hydrogen production from the water‐splitting process by hydrogen evolution reaction (HER) is a promising approach, but it needs commercially expensive Pt‐based electrocatalysts. Hence, designing nonnoble‐metal catalysts for this reaction with both great activity and robust stability has remained a crucial challenge in the past decades. In the present study, platinum (Pt), nickel (Ni), cobalt (Co), manganese (Mn), and copper (Cu) metals are uniformly considered on N‐doped carbon (NC) nanosheet derived from zeolitic imidazolate metal–organic framework (ZIF), and the capability of proposed nanomaterials is theoretically investigated by density functional theory (DFT) for HER process. As expected, results indicate Pt/NC has a high ability for HER process. Besides, it was shown that energy barriers in the HER process catalyzed by Ni and Co /NC electrocatalysts were close to zero representing their great HER activity.
First-Principles Study of Linear and Nonlinear Optical Properties of Multi-Layered Borophene

Anisotropic materials are of great interest due to their unique direction-dependent optical properties. Borophene, the two-dimensional analog of graphene consisting of boron atoms, has attracted immense research interest due to its exciting anisotropic electronic and mechanical properties. Its synthesis in several structural polymorphic configurations has recently been reported. The present work reports the layer-dependent optical absorption and hyperpolarizabilities of the buckled borophene (δ6-borophene). The results, based on density functional theory, show that multilayer borophene is nearly transparent with only a weak absorbance in the visible region, reflecting its anisotropic structural characteristics. The static first-order hyperpolarizability significantly increases with the number of layers, due mainly to interactions among the frontier orbitals in multilayer borophene. Transparency in the visible region combined with enhanced nonlinear optical properties makes the multilayer borophene important for future photonics technologies.
Comparative study of nursing education in three Middle East countries

Materials and Methods: This review -comparative study was conducted in 2019. Iran Health Ministry website and the site of several nursing schools in three countries and some related studies were used to reach the full details of the curriculum. Obtained information were analyzed by using Brady’s four-stage model including description, interpretation, proximity and comparison levels. Comparison was made in the areas of the time of the beginning, the mission, and the purpose of educating, admission criteria, and number of units, courses' orientations and structure.
Core mass-halo mass relation of bosonic and fermionic dark matter halos harboring a supermassive black hole

We study the core mass -- halo mass relation of bosonic dark matter halos, in the form of self-gravitating Bose-Einstein condensates, harbouring a supermassive black hole. We use the ``velocity dispersion tracing'' relation according to which the velocity dispersion in the core $v_c^2\sim GM_c/R_c$ is of the same order as the velocity dispersion in the halo $v_h^2\sim GM_h/r_h$ (this relation can be justified from thermodynamical arguments) and the approximate analytical mass-radius relation of the quantum core in the presence of a central black hole obtained in our previous paper [P.H. Chavanis, Eur. Phys. J. Plus 134, 352 (2019)]. For a given minimum halo mass $(M_h)_{\rm min}\sim 10^8\, M_{\odot}$ determined by the observations, the only free parameter of our model is the scattering length $a_s$ of the bosons (their mass $m$ is then determined by the characteristics of the minimum halo). For noninteracting bosons and for bosons with a repulsive self-interaction, we find that the core mass $M_c$ increases with the halo mass $M_h$ and achieves a maximum value $(M_c)_{\rm max}$ at some halo mass $(M_h)_{*}$ before decreasing. The whole series of equilibria is stable. For bosons with an attractive self-interaction, we find that the core mass achieves a maximum value $(M_c)_{\rm max}$ at some halo mass $(M_h)_{*}$ before decreasing. The series of equilibria becomes unstable above a maximum halo mass $(M_h)_{\rm max}\ge (M_h)_{*}$. In the absence of black hole $(M_h)_{\rm max}=(M_h)_{*}$. At that point, the quantum core (similar to a dilute axion star) collapses. We perform a similar study for fermionic dark matter halos. We find that they behave similarly to bosonic dark matter halos with a repulsive self-interaction, the Pauli principle for fermions playing the role of the repulsive self-interaction for bosons.
Contribution Analysis of the Streamflow Changes in Selected Catchments on the Loess Plateau, China, Using Multiple Budyko-Based Approaches

A better understanding of how streamflow interacts with climate change and human activities would contribute to the efficiency and effectiveness of water resources management. Specifically, quantifying the climate and human contributions has widely been used when attributing streamflow changes. However, only a few previous studies compared the results derived by different methods that are currently available, and even fewer studies have ever had a close look at the uncertainties induced by various estimations of evapotranspiration. This research first examined the streamflow changes for 12 catchments on the Loess Plateau in China during the period of 1961–2018 with Mann–Kendall test and relevant statistical measurements. Then, 8 Budyko-based climate elasticity methods, each with 13 estimations of evapotranspiration, were used to quantifying human and climate contributions to streamflow change in the study area (i.e., 104 pairs of values for human and climate contributions for one catchment). The results showed that significant declining trends could be found in 11 of the 12 catchments studied. In terms of contribution rates, human activity has been shown as the major contributor to the streamflow decrease (60–90%) compared to climate change (10–50%). By comparing the contribution results derived by possible combinations of attribution method and evapotranspiration estimation, the variability due to different Budyko-based methods being used seems to be related to geographical location and climate. Although the spatial pattern of variability due to different estimations of evapotranspiration is not obvious, it is necessary to consider the uncertainties induced when launching contribution analysis over specific regions.
Larval ecology, dispersal, and the evolution of sociality in the sea

Dispersal, or lack there of, has long been thought to play a crucial role in the evolution of sociality because individual dispersal decisions shape the kin structure of social groups (Brown, 1974; Emlen, 1982, 1994; Hatchwell, 2009; Koenig, 1981; Koenig et al., 1992; Mumme, 1996). As related individuals accumulate in space, kinselected benefits of helping relatives can promote the evolution of cooperative behaviors and sociality (Boomsma, 2009; Cornwallis et al., 2010; Hamilton, 1964; Lukas & CluttonBrock, 2012). Yet, at the same time, social interactions among group members influence dispersal decisions, and the potential for competition among kin can favor dispersal (Hamilton, 1964; Queller, 1994; Smith, 1964; Taylor, 1992; West et al., 2002). Ultimately, the propensity to disperse Received: 5 January 2021 | Revised: 14 April 2021 | Accepted: 7 May 2021 DOI: 10.1111/eth.13195
A Broadband Low-Profile Transmitarray Antenna by Using Differentially Driven Transmission Polarizer With True-Time Delay

A broadband low-profile folded transmitarray (FTA) is proposed and investigated, in which a novel wideband transmission polarizer (TP) is used to realize low-profile design. The proposed TP, mainly consisting of a differential-fed grid-slotted patch antenna, can realize high-efficiency transmission and polarization selection simultaneously within a wideband frequency range. Moreover, the proposed TP can realize linear transmission phase because of the true-time delay (TTD) technique. Owing to the function of polarization selection and linear transmission phase, the TP can be used as antenna element for a FTA. A 140-element FTA is designed and fabricated by combining the TP with a broadband polarization rotation reflecting surface (PRRS). The measured radiation patterns agree well with the simulated results, and a peak gain of 24 dBi and maximum aperture efficiency of 44.7% are achieved. Furthermore, −1 and −3 dB gain bandwidth with 25.9% and 33.3% are also experimentally demonstrated in the proposed FTA, respectively.
The Relationship between Service Quality, Physical Evidence and Customer Satisfaction in Sports Centres

Purpose: Ensuring customer satisfaction is one of the basic requirements of quality management systems and excellence models, is obtained by comparing before the customer buys the expected performance with the actual perceived performance. This study aimed to investigate the relationship between quantitative and physical evidence and service quality with customer satisfaction of sports facilities in District 5 of Tehran.Methodology: The research method is correlational in terms of implementation method. The statistical population includes all those who have used the services of sports facilities in District 5. 395 subjects were selected using cluster sampling as research samples. To collecting data, three questionnaires include quantitative and physical evidence (researcher-made), SERVQUAL (Parasuraman and Zithamel, 1988) and customer satisfaction (Kooshesh, 2014), was used. The validity of which was determined by 12 experts in the field of sports management was confirmed. Also, in a pilot study on 40 subjects using Cronbach's alpha, the reliability of the questionnaires was 0.86, 0.89 and 0.91, respectively.Findings: After the data collection, descriptive statistics, Kolmogorov-Smirnov tests, Pearson correlation, and multistage regression in SPSS software were used. The results showed a significant relationship between physical and quantitative characteristics and service quality with customer satisfaction. Also, there is an important relationship between customer satisfaction and the components of perceived factors, reliability, responsiveness, reliability and empathy. In addition, the variables of physical and quantitative characteristics and quality of services are significant predictors of the variable of customer satisfaction. Finally, suppose the quantitative and physical evidence and the quality of services of health clubs are at a suitable level. In that case, it can lead to customer satisfaction and more customer membership in that sports space.Originality: We showed the correlations between physical evidence, satisfaction, and loyalty in sports centres which were less noticed in previous studies.
Neoadjuvant double metal stent placement in a patient with locally unresectable cancer of the panceatic head—a case report

: Pancreatic cancer still has a poor prognosis and the only curative treatment that also leads to longest survival is surgical resection. However, due to poor performance status, locally advanced disease, or metastases only a minority of patients are candidates for surgery. On the other hand, newer and more potent neoadjuvant chemotherapy regimes may render locally advanced tumors resectable and when resection is achieved, resection results in improved prognosis. Cancer of the pancreatic head frequently cause biliary and duodenal obstruction that needs to be resolved prior to application of chemotherapy. Here we report the case of a 72-year-old patient who we diagnosed with cancer of the pancreatic head. At the time of diagnosis, cross-sectional imaging displayed no metastases. Histology of the tumor was confirmed by open surgery but the tumor was locally unresectable at the time of first exploration. Subsequently, the patient developed both jaundice and duodenal obstruction, therefore we performed “neoadjuvant” double metal stenting of the duodenum and the bile duct. The procedure involved an external-internal rendez-vous procedure that resulted in complete relief from biliary and gastric obstruction and enabled the patient to receive timely pre-operative chemotherapy.
Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion

The rapid and accurate identification of sugarcane stem nodes in the complex natural environment is essential for the development of intelligent sugarcane harvesters. However, traditional sugarcane stem node recognition has been mainly based on image processing and recognition technology, where the recognition accuracy is low in a complex natural environment. In this paper, an object detection algorithm based on deep learning was proposed for sugarcane stem node recognition in a complex natural environment, and the robustness and generalisation ability of the algorithm were improved by the dataset expansion method to simulate different illumination conditions. The impact of the data expansion and lighting condition in different time periods on the results of sugarcane stem nodes detection was discussed, and the superiority of YOLO v4, which performed best in the experiment, was verified by comparing it with four different deep learning algorithms, namely Faster R-CNN, SSD300, RetinaNet and YOLO v3. The comparison results showed that the AP (average precision) of the sugarcane stem nodes detected by YOLO v4 was 95.17%, which was higher than that of the other four algorithms (78.87%, 88.98%, 90.88% and 92.69%, respectively). Meanwhile, the detection speed of the YOLO v4 method was 69 f/s and exceeded the requirement of a real-time detection speed of 30 f/s. The research shows that it is a feasible method for real-time detection of sugarcane stem nodes in a complex natural environment. This research provides visual technical support for the development of intelligent sugarcane harvesters.
On the partial breaking of $\mathcal{N}=2$ rigid supersymmetry with a complex hypermultiplet

We study partial supersymmetry breaking in effective $\mathcal{N}=2$ U$ \left( 1\right) ^{n}$ gauge theory coupled to complex hypermultiplets by using the method of L. Andrianopoli et al., Phys. Lett. B 744, 116 (2015), which we refer to as the ADFT method. We derive the generalization of the symplectic invariant ADFT formula $\zeta _{a}=\frac{1}{2}\varepsilon _{abc}(\mathcal{P}^{bM}\mathcal{C}_{MN}\mathcal{P}^{cN}) $, capturing information on partial breaking. Our extension of this anomaly is expressed as $d_{a}=\frac{1}{2}\varepsilon _{abc}\mathbb{P}^{bM}\mathcal{C}_{MN}\mathbb{P}^{cN}+\mathcal{J}_{a}$. The generalized moment maps $\mathbb{P}^{aM}$ contain $\mathcal{P}^{aM}$ and also depend on electric/magnetic coupling charges $G^{M}=( \eta ^{i},g_{i}) $; the $\mathcal{J}_{a}$ is an extra contribution induced by Killing isometries in the complex hypermatter sector. Using SP$\left(2n,\mathbb{R}\right)$ symplectic symmetry, we also give the $\mathcal{N}=2$ partial breaking condition and derive the model of I. Antoniadis et al., Nucl. Phys. B 863, 471 (2012) by a particular realization of the $d_{a}$ anomaly.
Topp-Leone distribution with an application to binomial sampling

Abstract In this paper, we present the exact distribution of the sum of two independent Topp-Leone variables which becomes useful in calculating the exact reliability of a standby system with Topp-Leone distributed component units when the values of parameters are known. Applications in Bayesian analysis with binomial sampling are also considered, and the comparison of the performance of Beta and Topp-Leone priors is then made.
A hybrid recognition model of microseismic signals for underground mining based on CNN and LSTM networks

Abstract Microseismic (MS) monitoring technology has been widely used to monitor ground pressure disasters. However, the underground mining environment is complex and contains many types of noise sources. Furthermore, the traditional recognition method entails a complex process with low recognition accuracy for MS signals, so it is difficult to serve for the safe production of mines. Therefore, this study established a hybrid model combining the singular spectrum analysis (SSA) method, convolutional neural networks (CNN), and long short-term memory networks (LSTM). First, the principal components of monitoring signals were extracted with the SSA method, and then spatial and temporal features of monitoring signals were separately extracted with the CNN and LSTM. Based on actual field data collected from Xiadian Gold Mine, the hybrid model was compared with the CNN, LSTM, and back-propagation networks (BP), as well as commonly used recognition methods including the support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and linear discriminant analysis (LDA). The results show that the proposed hybrid model can accurately extract data features of monitoring signals and further improve MS signals' recognition performance. Furthermore, the recognition accuracy of mechanical signals in monitoring signals is particularly increased using the hybrid model, which avoids confusion with MS signals.
Predictive rental values model for low-income earners in slums: the case of Ijora, Nigeria

Abstract It is well known most often that values of properties tend to hike at the effluxion of time. This has necessitated the adoption of predictive models in interpreting outcomes in the property market in the future. Earlier studies have been oblivious of such models' outcomes as it affects any focal group, particularly the vulnerable. This present study focuses on the low-income earners found in the slum. The Ijora community in Lagos was the highlight of this study, particularly Ijora Badia and Ijora Oloye, regarded as slums according to the UNDP report. The entire fifty-two (52) local agents in the Ijora community were surveyed in cross-sectional survey research that entailed the questionnaire's issuance. The nexus of data collection, pre-processing, data analysis, algorithm application, and model evaluation resulted in retrieving rental values within the years 2010 and 2019 on two predominant residential property types of self-contain and one-bedroom flats found within the community. Three selected algorithms, Artificial Neural Network (ANN), Support Vector Machine, and Logistic Regression, were essentially used as classifiers but trained to predict the continuous values. These algorithms were implemented through the use of Python's SciKit-learn Library and RapidMiner. The findings revealed that though all three models gave accurate predictions, Logistic Regression was the highest with low error values. It was recommended that Logistic Regression be applied but with much data set of property values of low-income earners over much more period. This study will contribute to the Sustainable development goals(SDG) 11(Sustainable cities and communities) of the United Nations to benefit developing countries, especially in sub-Saharan Africa.
Zebra finch song is a very short-range signal in the wild: evidence from an integrated approach

Abstract Birdsong is typically seen as a long-range signal functioning in mate attraction and territory defense. Among birds, the zebra finch is the prime model organism in bioacoustics, yet almost exclusively studied in the lab. In the wild, however, zebra finch song differs strikingly from songbirds commonly studied in the wild as zebra finch males sing most after mating and in the absence of territoriality. Using data from the wild, we here provide an ecological context for a wealth of laboratory studies. By integrating calibrated sound recordings, sound transmission experiments and social ecology of zebra finches in the wild with insights from hearing physiology we show that wild zebra finch song is a very short-range signal with an audible range of about nine meters and that even the louder distance calls do not carry much farther (up to about fourteen meters). These integrated findings provide an ecological context for the interpretation of laboratory studies of this species and indicate that the vocal communication distance of the main laboratory species for avian acoustics contrasts strikingly with songbirds that use their song as a long-range advertisement signal.
Political Leaders’ Communication: A Twitter Sentiment Analysis during Covid-19 Pandemic

The pandemic made it critical for political leaders to intensify measures in fight against Covid-19 and one such measure was building trust among public through communication. With exponential growth in reach of social media, while state political leaders have progressively used internet for election campaigns, limited studies have explored as to how leaders use this medium to communicate during crisis, what kind of information do they share and what are common issues addressed. This paper, using qualitative research design, analyses Indian political leaders’ communication on Twitter. Sentiment Analysis was carried to identify and extract subjective information in leaders’ communication using 29 Indian political leaders, wherein 12,128 tweets were extracted. Subjectivity scores depicted more than half of leaders had shared fact-based information, and Polarity scores indicated that almost 90% of leaders shared positive or neutral information thus leading to an inference that leaders share more of facts based and positive or neutral information rather than statements in form of opinions.
Prediction of Electromagnetic Characteristics in Stator End Parts of a Turbo-Generator Based on MLP and SVR

In order to study the multiple restricted factors and parameters of the eddy current loss of generator end structures, both the multi-layer perceptron (MLP) and support vector regression (SVR) are used to study and predict the mechanism of the synergistic effect of metal shield conductivity, relative permeability of clamping plates and structural characteristics of eddy current losses. Based on the eddy current losses of generator end structures under different metal shielding thicknesses and electromagnetic properties, the calculation accuracy of the MLP and SVR is compared. The prediction method gives an effective means for the complex design of the end region of the generator, which reduces the effort of the designers. It also promotes the design efficiency of the electrical generator.