Miracle-dz/UniFAR-FTUs huggingface dataset:
Mixed Convection in an Isothermally Horizontal Channel with Packed Spheres

This study ignores the nonlinear inertia terms of non-Darcian mixed convection in an isothermally horizontal channel with packed spheres (stainless sphere and water). The important characteristics of non-Darcian convection are the no-slip boundary, flow inertia, channeling effect and thermal dispersion effect. The ranges of the Rayleigh number Ra= 0~ 5 10
Demographic Factors Associated with Attitude of Youth Towards Agripreneurship

Agricultural entrepreneurship especially among youth is essential for employment creation. Entrepreneurial attitude is identified asa key driver to agricultural entrepreneurship and it seems to be associated with demographic factors. Research results on demographic factors still require further research which necessarily add more insight to the area of study. Hence the present study is aimed at evaluating the entrepreneurial attitude of youth in Lesotho and demographic factors that could have an influence on such attitude. A quota convenience sampling technique was used to select a sample from the National University of Lesotho final year undergraduates which resulted in attaining 78.8 percent response rate. A structured questionnaire was used for data collectionwhile descriptive statistics was used for data analysis. The results demonstrated that the majority of youth exhibited a negative attitude towards agricultural entrepreneurship therebysignifying the need for interventions that would ignite positive attitude to motivate agriculturalentrepreneurial activity in the country.All demographic factors with the exception ofone werefound to be significant predictors of entrepreneurial attitudeinherently useful in segmenting youth market if entrepreneurial interventions are implemented.
Effect of meat and bone meal (MBM) and Bacillus subtilis on the content of nitrogen and phosphorus in soil and white mustard biomass

The aim of this pot experiment was to determine the effect of increasing rates of meat and bone meal (MBM), applied alone or in combination with a microbial preparation containing Bacillus subtilis (FITOdoctor), on the growth of Sinapis alba L. Dry matter (DM) yield, the nitrogen (N) and phosphorus (P) content of white mustard were determined, together with the mineral N (Nmin) and available P content of soil. The application of FITOdoctor to the soil improved white mustard yield only during the second growing season. Second-harvest white mustard biomass had a significantly lower content of N and P than first-harvest biomass. The highest MBM rate significantly increased the Nmin content of soil. Nitrogen uptake by plants and N concentration in biomass were highest in the treatments with the highest mineral N content of soil. Similar relationships were observed for P whose uptake by plants was also affected by B. subtilis in two treatments (NPK, 0.8% MBM). MBM is a valuable N and P fertilizer, whereas B. subtilis has varied effects.
On Secure Downlink NOMA Systems With Outage Constraint

In this work, we investigate the relationship between the reliability and security of a typical two-user downlink non-orthogonal multiple access (NOMA) communication system. The level of successive interference cancellation on NOMA user is considered. Firstly, the impact of various key parameters on transmit signal-to-noise ratio (SNR) of the NOMA users with the reliability outage probability (ROP) constraint is discussed. Taking the minimum of transmit SNR for ROP into account, the secrecy outage performance of the downlink NOMA systems is studied and the analytical expressions of the secrecy outage probability of the NOMA system are derived under two cases of eavesdropping capability. Furthermore, the sum effective secrecy throughput of the NOMA system under two scenarios with considering ROP constraint is derived. Monte Carlo simulations are provided to verify the accuracy of our analysis.
Aluminum Toxicity Symptoms in Peach Seedlings1

‘Elberta’ (Prunus persica (L.) Batsch) peach seedlings were grown in nutrient solutions for 27 days with aluminum concentrations of 0, 222, 666 and 2000 μm; the 2000 μm concentration induced A1 toxicity symptoms in leaves and severely restricted root growth. The early stage of A1 toxicity was characterized by marginal chlorosis that later developed into necrotic areas that extended along the veins toward the midrib. Advanced stages of toxicity were characterized by collapse of the midrib, terminal dieback and defoliation of the seedlings which are typical symptoms of calcium deficiency in peaches. At high A1 concentrations roots died back and new roots developed as irregularly shaped cylinders with constrictions and enlargements at the root apex.
Deep Feature Collaboration for Challenging 3D Finger Knuckle Identification

Contactless 3D finger knuckle pattern is a new biometric identifier which offers highly discriminative features for the finger knuckle based personal identification. State-of-the-art methods for object recognition, a more generic problem, employ deep neural network based approaches and demonstrate superior effectiveness. However, any direct applications from those methods do not outperform specialized hand-crafted feature description approaches for the problem addressed in this paper. In addition, such deep neural network based methods have to address challenges associated with emerging biometrics, e.g. availability of very limited training data, large intra-class or train-test sample variations as observed for the real applications, etc. This paper attempts to address the above challenges and introduces a new deep neural network based approach for the contactless 3D finger knuckle identification. Our approach simultaneously encodes and incorporates deep features from multiple scales to form a more robust deep feature representation. Such collaborative feature representations are robustly matched using an efficient alignment scheme with a fully convolutional architecture to accommodate involuntary finger variations during the contactless imaging. Comparative experimental results in the two-session 3D finger knuckle images database, acquired from over 200 subjects and is publicly introduced from this paper, illustrate superior performance over the state-of-the-art methods, e.g. offering ~22% GAR improvement at extremely low FAR under challenging comparison scenarios. Additional experiments in other publicly available databases including 3D palmprint, 3D fingerprint, and 2D finger knuckle further validate the effectiveness and demonstrate the generalizability of the proposed approach.
Corporate social responsibility disclosure to firm value with family ownership as the moderating variable

The growing concern on social and environmental issues from various stakeholders creates the demand for corporate social responsibility (CSR) disclosures. This study aims to obtain empirical evidence on the association of CSR disclosure and firm value with family ownership as the moderating variable. It is argued that family firms will strengthen the association as family owners will try to keep the good reputation of their company and maintain good relationships with stakeholders. The quantitative method is used to investigate this issue by performing a moderated regression on a sample of manufacturing companies in Indonesia. The number of samples is 196 firm-year observations for the research period of 2014–2016, selected using a purposive sampling technique. The results show that CSR disclosure had a positive effect on firm value, but there was no role of moderation of family ownership variables found on the association. The insignificant role is likely due to the effect of weak control from the pyramid-shaped ownership on firms in Indonesia. The contribution of this research is expected to provide an overview of CSR disclosure and its relation to firm value in the context of family companies in Indonesia.
Spillover Index Approach in Investigating the Linkage between International Tourism and Economic growth in Central and Eastern European Countries

The present study analyses the relationship between economic growth and tourism growth at the level of Central and East European countries, using the spillover indices approach. Based on the monthly data obtained for the period 2000–2019, the analysis of this paper presents certain empirical results. Firstly, the relationship economic growth-international tourism grow is not stable over time, both from the point of view of its size and its direction, which suggests that the specific activities of international tourism contribute to the economic growth and hypotheses according to which international tourism growth causes economic growth are time-dependent. Secondly, the relationship economic growth-international tourism growth is dependent on certain major events, such as the economic and financial crisis that started in 2008 and the debt crises from 2010. The results obtained show that the impact of these events influences the direction of the relationship between international tourism and economic growth which becomes more accentuated during the economic growth periods.
Competition, Profit Share and Concentration

This paper investigates the distributional impact of ’winner-takes-most’ competition and its role in shaping recent macroeconomic trends in advanced economies. We document a positive correlation between variations in industry labor and capital shares, and a positive correlation between variations in industry profit shares and industry concentration levels. However using micro-based industry data on firm-level profit margins, we find a negative correlation between industry concentration and a wide range of moments from the distribution of profit shares. We propose a dynamic general equilibrium model with heterogeneous firms, in which an increase in competition, whereby consumers become more price-sensitive and firm markups decrease, leads to a rise in concentration, a decrease in firm-level profit shares but an increase in industry-level profit shares. We study the effect of a change in the competitive environment on the Balanced Growth Path (BGP). In contrast with representative firm models, competition reduces the probability of successful entry and product diversity. If consumers value product diversity, we show that output growth, the natural interest rate, and welfare decrease with competition.
Montane seasonal wetlands: an inventory of its associated flora in Parque Estadual do Ibitipoca, southeast Brazil

The aim of the present study was to perform the floristic survey of the wetlands of the Parque Estadual do Ibitipoca (PEIB), comparing them regarding richness, floristic composition and life forms, based on monthly expeditions to collect vascular plants. Eighty-one (81) species were found in the moist grassland, most of them being endemic to Brazil and included in 55 genera and 21 families. The most representative families were Asteraceae (22 spp.), Melastomataceae (nine spp.), Xyridaceae (seven spp.), Poaceae and Orchidaceae (six spp. each), totalizing 62% of the sampled species. Twenty-seven (27) species were found in the seasonal pond, distributed in 23 genera and 11 families. Families with the highest specific richness were Poaceae (eight spp.), Cyperaceae and Asteraceae (five spp. each), totalizing 67% of the sampled species. There was a predominance of hemicryptophyte life form (nearly 43% of the moist grasslands and 63% of the seasonal pond) in both areas. The floristic analysis results showed great importance to conservation with 22% of species under some threatened category, 12 species endemics to Minas Gerais state and one new species of Cyperaceae. Furthermore, only five species occur in both wetland types (5.1% of the species richness).
An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak

With the recent COVID-19 outbreak, we have assisted to the development of new epidemic models or the application of existing methodologies to predict the virus spread and to analyze how the different lock-down strategies can effectively influence the epidemic diffusion. In this paper, we propose a novel machine learning based framework able to estimate the parameters of any epidemiological model, such as contact rates and recovery rates, based on static and dynamic features of places. In particular, we model mobility data through a graph series whose spatial and temporal features are investigated by combining Graph Convolutional Neural Networks (GCNs) and Long short-term memories (LSTMs) in order to infer the parameters of SIR and SIRD models. We evaluate the proposed approach using data related to the COVID-19 dynamics in Italy and we compare the forecasts of the trained model with available data about the epidemic spread.
Challenges in Transitioning From Clinician to Educator for Occupational Therapy Assistant Program Faculty Members

There is a transitional process that occurs when an occupational therapy (OT) practitioner assumes the role of an educator. Various challenges have been reported, though few directly address occupational therapy assistant (OTA) education. This study aimed to identify challenges faced by OT practitioners when they transition from working in a clinical setting to working as OTA faculty members. A 29-item online survey was developed covering three key areas of new OTA faculty members’ responsibilities: a) Learning the Educator Role, b) Curriculum and Class Development, and c) Interacting with Students. The survey was distributed through the American Occupational Therapy Association Program Directors’ and Academic Fieldwork Coordinators’ listservs, the Education Special Interest Section listserv and direct email to faculty members. Two hundred twenty-three participants completed the survey. Eight items were rated by 60% of participants as “Moderately” or “Very” challenging. Six of these items were in the category of Curriculum and Class Development, and two items were in the category of Learning the Educator Role. Interacting with Students was a relative area of comfort and strength. No significant relationships were found between the level of challenge experienced by OT practitioners transitioning to OTA educators and the demographic factors that were examined. Content analysis of open-ended survey questions yielded additional themes in each of the three categories that offer deeper insights into specific challenges. Collectively, results highlight areas where support and professional development can enhance successful clinician to OTA educator transitions.
Antimicrobial activity of the ethanolic extract of propolis against bacteria that cause mastitis in cattle

A própolis é uma substância coletada por abelhas, especialmente pela Apis mellifera, a partir de resinas de brotos de plantas que possuem numerosas propriedades biológicas, como antimicrobiana e antiinflamatória. Atividades como a pecuária leiteira orgânica apresentam alta demanda por produtos naturais com propriedades como as presentes na própolis, uma vez que a contaminação do leite e a mastite são grandes problemas da indústria de laticínios. O objetivo deste trabalho foi avaliar a atividade antimicrobiana in vitro do extrato etanólico de própolis (EEP) em diferentes concentrações. Ação do EEP extraído de colmeias no sul do Brasil foi testada contra 13 diferentes gêneros de agentes bacterianos causadores de mastite em bovinos leiteiros. O experimento foi conduzido no laboratório de Parasitologia e Microbiologia da Universidade Federal do Pampa, Campus Dom Pedrito – RS, durante os meses de maio e junho de 2016. Seguiram-se as técnicas de difusão em ágar e microdiluição em placas. Através dos resultados do teste de concentração inibitória mínima (CIM), pode-se afirmar que o EEP possui atividade inibitória em 100% das bactérias testadas em concentrações acima de 10% (p/v). Tais resultados mostram que a própolis apresenta potencial antimicrobiano frente as principais bactérias envolvidas nos processos de mastite.
Determinants of Students’ Perceived Learning Outcome and Satisfaction in Online Learning during the Pandemic of COVID19

The COVID19 pandemic has disrupted the normal functioning of various activities across the world, including learning and education The shift towards online education during the pandemic of COVID19 has led many studies to focus on perceived learning outcomes and student satisfaction in this new learning environment This study aims to examine the determinants resulting in students' perceived learning outcomes and their influence on student satisfaction The data was collected from undergraduate students in both South Korea and India to gain a cross-country study The study found that the factors-interaction in the classroom, student motivation, course structure, instructor knowledge, and facilitation-are positively influencing students' perceived learning outcome and student satisfaction There is no significant difference in the students' perceived learning outcome and student satisfaction in the two countries The study will be helpful for the educationists and academics to identify the factors which will enhance student learning outcome and satisfaction level in online classes during the coronavirus pandemic © 2020 by the authors;licensee Asian Online Journal Publishing Group
Cross-Modal Stream Scheduling for eHealth

Cross-modal applications that elaborately integrate audio, video, and haptic streams will become the mainstream of the eHealth systems. However, existing stream schedulers usually fail to simultaneously meet the cross-modal transmission requests in terms of low latency, high reliability, high throughput, and low complexity. To circumvent this dilemma, this article proposes a general cross-modal stream scheduling scheme by fully taking advantage of the characteristics of different modal streams and their underlying temporal, spatial, and semantic relevance. Specifically, we first propose a hierarchical stream category framework, in which the transmission priority of the modal stream instead of the data flow can be flexibly settled. Next, we design a series of modal-aware stream scheduling schemes by jointly making use of the network slice and mobile edge computing to achieve the tradeoff among the various metrics. Importantly, the transmission strategy can be adjusted adaptively to realize the optimal resource allocation. Subsequently, we analyze the relationship among the user experience, multi-modal impact, and stream scheduling through investigating the interacted impacts among the different modal streams, then develop a user experience based scheduling switch strategy to improve the application generality and reduce the performance fluctuation. Numerical objective and subjective results demonstrate the efficiency of the proposed cross-modal scheduling scheme.
The Productivity of Selected Species and Cultivars of Legumes Grown for Seeds in Organic Production System

The aim of the study was to assess the yielding of selected legume species with diversified morphological structure cultivated for seeds in ecological system. The field experiment was carried out in 2016–2018. The first factor was legume species: faba bean, field pea, yellow lupine, and blue lupine, and the second factor was varieties of legumes: faba bean (Granit and Amulet), field pea (Hubal and Batuta), blue lupine (Kurant and Regent), and yellow lupine (Bursztyn and Perkoz). After the harvest, the grain yield of legume plants and the weight of a thousand seeds were determined. The plant structure was determined (length of the part of fruiting stem, number of pods and seeds per plant, number of seeds in the pod, number of fruiting nodes, number of pods and seeds from the node). In addition, the content of selected nutrients (protein, fiber, fat, macroelements) was determined in seeds. Studies showed that in ecological conditions, the pea cultivation, especially Hubal variety (with bipinnate leaves), enabled obtaining the largest seed yield, while the smallest seed yields yellow lupine independent of the morphological type. The selfcompleting varieties of faba bean, yellow lupine, and blue lupines were yielded at a higher level than varieties with a traditional growth type. Among the pea varieties assessed, the variety Hubal yielded better (with bipinnate leaves). Significantly, higher yield of protein is provided by faba bean cultivation, while the smaller level of pea and yellow lupine.
Camelliol C inhibits viability, migration, and invasion of human cervical cancer cells via induction of apoptosis, G2/M cell cycle arrest, and blocking of PI3K/AKT signalling pathway

Cervical cancer is a devastating cancer and is currently ranked as the fourth most prevalent type of cancer in the world. The lack of efficacious chemotherapy forms a bottleneck in the treatment of cervical cancer. This study was therefore undertaken to evaluate the anticancer effects of camelliol C against human cervical cancer cells.Normal cell line (NCEC) and cervical cancer cell lines (Csdki, HeLa, C33A, and siHa) were used in this study. Cell viability was determined by MTT assay, and apoptosis was detected by DAPI and annexin V/PI staining. Cell cycle analysis, ROS, and MMP levels were examined by flow cytometry. Cell migration and invasion was monitored by transwell assays.The results showed that camelliol C inhibits the proliferation of all the human cervical cancer cell lines with IC50 ranging from 10 to 20 µM. However, comparatively low antiproliferative effects were observed on the normal cells. Investigation of the underlying mechanisms showed that camelliol C induces apoptosis in HeLa cancer cells. Camelliol C-triggered apoptosis was also linked with cleavage of caspase-3 and -9, and PARP. Additionally, the Bax protein levels were increased and those of Bcl-2 were decreased. Transwell assays showed that camelliol C suppresses the migration and invasion of the HeLa cervical cancer cells. Additionally, camelliol C also blocked the PI3K/AT signalling cascade dose dependently.Camelliol C may prove beneficial in the treatment of cervical cancer.
Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing Based on Complex-Valued ADMM-Net

Sparse aperture radar imaging is generally achieved by methods of compressive sensing (CS), or, sparse signal recovery(SSR). However, most of the traditional SSR methods cannot produce focused image stably, which limits their applications. <inline-formula> <tex-math notation="LaTeX">${l}_{1}$ </tex-math></inline-formula> regularization and alternating direction method of multipliers(ADMM) are generally applied to the SSR problem, but its performance is sensitive to the selection of model parameters. This paper proposes a complex-valued ADMM-Net(CV-ADMMN) method to improve the stability of ADMM, and utilize it to achieve sparse aperture ISAR imaging and autofocusing. Firstly, the iterative procedure of ADMM is unrolled to be a deep network structure. Then, the parameters of the model are learned from a training dataset by utilizing an <inline-formula> <tex-math notation="LaTeX">${l}_{1}$ </tex-math></inline-formula> regularized loss function. Finally, an autofocusing module based on entropy-minimization is plugged into the trained model to compensate the phase error. Experimental results based on both simulated and measured data validate the superiority of the proposed method over ADMM.