Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of an Effective University Professor From Students’ Perspective: Are the Qualities Changing?

This study investigates the characteristics of an effective university professor based on the evaluations made by students in different majors at a state university in Iran. Two-hundred forty BA, MA, and Ph.D. students’ evaluations of their teachers were selected via purposive sampling. The evaluations were then content analyzed to determine which characteristics build the profile of an effective teacher in the students’ eyes. The results confirmed the findings of many previous studies that a good university professor needs to possess certain essential qualities. However, the profile of an effective university professor, at least the importance of the qualities that make up this profile, was rather different. More specifically, the most important criterion for evaluating the teachers was their assessment policies and practices. Furthermore, the findings suggest that the characteristics of an effective professor are dynamic and open to contextual, cultural and temporal influences. In light of the results of this study, it is recommended that higher education institutions put in place programs that educate teachers about a more learner-centered pedagogy to maximize not only their own teaching efficacy but also their students’ motivation and learning.
Transcriptomic Profiling of Circular RNAs in the Goat Rumen During Fetal and Prepubertal Period

Circular RNAs (circRNAs) are key regulatory factors with vital functions in various biological activities. However, little has been reported concerning the genetic regulation of circRNAs during rumen development in goats. The aim of this study was to identify the genome-wide expression profiles of circRNAs in the rumen of goats during fetal development and before and after weaning. Histological morphology showed that from the fetal period (days 60 and 135 of gestation) to the prepuberal period (days 60 and 150 of age) the rumen papilla developed gradually, and the thickness of the rumen muscular layer increased. A total of 11,149 circRNAs were identified in the four development stages by RNA-sequencing. From this, 1,518 were differentially expressed circRNAs (DECs). Fifty-eight DECs were up-regulated from 60 to 135 days of gestation, and 93 from day 135 of pregnancy to 30 days after birth. A large proportion (598) of DECs were down-regulated from day 135 of gestation to 30 days after birth. The expression levels of six randomly selected circRNAs were validated by qPCR, and their back-splicing junction (BSJ) sites were also confirmed. Ontology and pathway analyses revealed that the parental genes of DECs were mainly involved in the signaling pathways related to cell proliferation and apoptosis. The interaction network of circRNAs with their target miRNAs showed its involvement in cell proliferation and apoptosis signaling pathways. In conclusion, we identified the genome-wide expression profiles of circRNAs in the rumen of goats during fetal development and before and after weaning. These results provide a basis for further study on the regulatory effect of circRNAs on the development of rumen tissues.
Research on the Influence Mechanism of Enterprises’ Participation in School Enterprise Cooperation Based on the Analysis Framework of Theory of Planned Behavior

School enterprise cooperation, as the basic school running form of applied undergraduate education, is an important way to cultivate applied talents. However, at present, the lack of motivation for enterprises to participate in school enterprise cooperation and the resulting problem of “school hot and enterprise cold” seriously limit the talent training quality in China’s application-oriented universities. There is an urgent need to explore the influencing factors and mechanisms of enterprises’ participation in school enterprise cooperation to improve the training quality of applied talents. Taking Ajzen (2002) Theory of Planned Behavior as the theoretical framework, this study constructs the influencing factor model of enterprise participation in school enterprise cooperation from four aspects, namely, behavior attitude, subjective norms, perceived behavior control, and behavior intention. In this study, participants (N = 250) completed a questionnaire assessing their participation in school enterprise cooperation, which was analyzed by a structural equation model. The results show that the behavior attitude and perceived behavior control of enterprises have a significant positive impact on their intention to participate in school enterprise cooperation and then have a significant positive impact on the school enterprise cooperation behavior of enterprises. The behavior intention and perceived behavior control of enterprises have a significant positive impact on their participation in school enterprise cooperation. The policy environment has a significant regulatory effect on the relationship between the intention and behavior of enterprises’ participation in school enterprise cooperation. Therefore, from the perspective of enhancing the intention of enterprises to cooperate, colleges and universities should establish the awareness of win-win cooperation and meet the interest demands of enterprises in school enterprise cooperation in order to improve the behavior attitude and intention of enterprises. From the perspective of the formation conditions of school enterprise cooperation, with the help of industry associations, an information service platform for school enterprise cooperation should be built in order to eliminate the information islands between enterprises and universities. From the perspective of the needs of school enterprise cooperation environment, government departments should strengthen the policy support for school enterprise cooperation in order to eliminate the worries of enterprises’ participation in school enterprise cooperation.
Monitoring Changes and Soil Characterization in Mangrove Forests of the United Arab Emirates Using the Canonical Correlation Forest Model by Multitemporal of Landsat Data

Mangrove forests are an important indicator of blue carbon storage and biodiversity and provide several benefits to the environment. This study showed the first attempt to apply the canonical correlation forest (CCF) model to classify mangroves and monitor changes in the mangrove forests of the entire region. The CCF model obtained a satisfactory accuracy with an F1 score of more than 0.90. Compared to Sentinel-2, Landsat 8 exhibited good temporal resolution with relatively little mangrove details. The resultant mangrove maps (1990–2020) were used to monitor changes in mangrove forests by applying a threshold value ranging from +1 to −1. The results showed a significant increase in the UAE mangroves over the period from 1990 to 2020. To characterize soil in mangrove forests, a set of interpolated maps for calcium carbonate, salinity concentration, nitrogen, and organic matter content was constructed. The results showed that there is a positive relationship between mangrove distribution and the calcium carbonate, nitrogen, salinity, and organic matter concentrations in the soil of the mangrove forests. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization.
The Inclusion of Alhagi maurorum in Growing Camel Diet: Effect on Performance, Liver-Related Blood Metabolites, and Antioxidant Status

This study determined the effect of dietary inclusion of camelthron [Alhagi maurorum (AM)] on the performance, blood metabolites, and antioxidant status of growing camels. A total of 18 Sindhi camel calves of 9–10 months of age and 115 ± 7 kg body weight (BW) were randomly assigned to three diets (with a forage:concentrate ratio of 50:50) that were formulated by partial and total substitution of alfalfa hay with AM as follows: (1) diet without AM (control), (2) diet containing 25% of AM (AM-25), and (3) diet containing 50% of AM (AM-50) (dry matter basis) for 150 days. Dry matter intake (DMI) was recorded daily. The camels were weighed individually on days 0, 30, 60, 90, 120, and 150. Blood samples were collected on days 0, 75, and 150. DMI was increased (p = 0.004) with AM-50 feeding followed by AM-25. Total weight gain (p = 0.048) and average daily gain (ADG) (p = 0.043) decreased with AM-50; however, no differences were observed between the AM-25 and CON groups. Feed cost per kg BW gain tended to decrease (p = 0.092) and return per kg BW gain tended to increase (p = 0.087) by AM feeding. The plasma triglycerides (TGs) (p = 0.046) and cholesterol (CHOL) (p = 0.025) concentration were reduced with AM inclusion. Additionally, the AM50-fed camels showed the lowest concentration of aspartate aminotransferase (AST) (p = 0.008) and alanine aminotransferase (ALT) (p = 0.0036), followed by AM-25. The plasma malondialdehyde (MDA) was depressed (p = 0.037) and total antioxidant capacity (TAC) was enhanced (p = 0.016) with both the AM-25 and AM-50. Moreover, feeding the AM containing diets led to higher (p = 0.004) glutathione peroxidase (GPx) along with a tendency for superoxide dismutase (SOD) (p = 0.075) and catalase (CAT) (p = 0.094). Overall, feeding camels with AM for up to 25% of their dry matter (DM) diet positively influenced the antioxidant status without severe deleterious effects on performance.
Analysis and Prediction of Ice Shedding for a Full-Scale Heated Tail Rotor

When helicopters are to fly in icing conditions, it is necessary to consider the possibility of ice shed from the rotor blades. In 2013, a series of tests were conducted on a heated tail rotor at NASA Glenn's Icing Research Tunnel (IRT). The tests produced several shed events that were captured on camera. Three of these shed events were captured at a sufficiently high frame rate to obtain multiple images of the shed ice in flight that had a sufficiently long section of shed ice for analysis. Analysis of these shed events is presented and compared to an analytical Shedding Trajectory Model (STM). The STM is developed and assumes that the ice breaks off instantly as it reaches the end of the blade, while frictional and viscous forces are used as parameters to fit the STM. The trajectory of each shed is compared to that predicted by the STM, where the STM provides information of the shed group of ice as a whole. The limitations of the model's underlying assumptions are discussed in comparison to experimental shed events.
Pore Structure and Wettability of Lacustrine Shale With Carbonate Interlayers in Dongying Depression, Bohai Bay Basin, East China

Shale samples with carbonate interlayers have attracted more and more attention in shale oil exploration of lacustrine shale in China, and the characterization of pore structure and wettability of these shales are significant to the study of shale-oil enrichment and effective exploitation. In this work, by examining six shale samples with carbonate interlayers of Shahejie Formation in East China, the pore structure and wettability characteristics of shale are characterized by means of thin section petrography; X-ray diffraction mineralogy; total organic carbon (TOC) analyses; scanning electron microscopy (SEM) imaging; air-liquid contact angle for wettability; as well as N2 physisorption, mercury intrusion porosimetry, and nuclear magnetic resonance (NMR) for pore structure. The results show that the main mineral contents are carbonate (with an average of 51.4%) and clay minerals (mainly mixed-layer illite-smectite). The average TOC content is 2.90%, and there is a strong correlation between TOC and dolomite content. In addition, the obvious layered structure is observed by thin section and SEM methods. The pores below 200 nm with ink-bottle shapes are obviously smaller than those of marine shale, and the pore throats are mainly below 50 nm; however, there are also some micrometer-sized cracks. The droplet contact angle measurement shows that the shale is mainly lipophilic, while moderately hydrophilic, at millimeter observational scales. The NMR T2 spectra of water- and oil-saturated samples have an obvious feature of three peak characteristics, as the pore size-associated wettability of these samples can be divided into three stages: water-wet (0.01–1 ms), oil-wet (1–40 ms), and mixed-wet (>40 ms) in terms of relaxation time of the NMR T2 spectrum. The proportion of the second main peak of T2 spectra (P2) for dodecane-saturated samples is directly proportional to the TOC content, and the relationship between P2 and mineral composition is consistent with water-saturated samples.
Decoding Genetic Markers of Multiple Phenotypic Layers Through Biologically Constrained Genome-To-Phenome Bayesian Sparse Regression

The applicability of multivariate approaches for the joint analysis of genomics and phenomics information is currently limited by the lack of scalability, and by the difficulty of interpreting the related findings from a biological perspective. To tackle these limitations, we present Bayesian Genome-to-Phenome Sparse Regression (G2PSR), a novel multivariate regression method based on sparse SNP-gene constraints. The statistical framework of G2PSR is based on a Bayesian neural network, were constraints on SNPs-genes associations are integrated by incorporating a priori knowledge linking variants to their respective genes, to then reconstruct the phenotypic data in the output layer. Interpretability is promoted by inducing sparsity on the genes through variational dropout, allowing to estimate the uncertainty associated with each gene, and related SNPs, in the reconstruction task. Ultimately, G2PSR is conceived to prevent multiple testing correction and to assess the combined effect of SNPs, thus increasing the statistical power in detecting genome-to-phenome associations. The effectiveness of G2PSR was demonstrated on synthetic and real data, with respect to state-of-the-art methods based on group-wise sparsity constraints. The application on real data consisted in an imaging-genetics analysis on the Alzheimer’s Disease Neuroimaging Initiative data, relating SNPs from more than 3,500 genes to clinical and multi-variate brain volumetric information. The experimental results show that our method can provide accurate selection of relevant genes in dataset with large SNPs-to-samples ratio, thus overcoming the main limitations of current genome-to-phenome association methods.
Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries

Background Hypertension is the most common modifiable risk factor for cardiovascular diseases in South Asia. Machine learning (ML) models have been shown to outperform clinical risk predictions compared to statistical methods, but studies using ML to predict hypertension at the population level are lacking. This study used ML approaches in a dataset of three South Asian countries to predict hypertension and its associated factors and compared the model's performances. Methods We conducted a retrospective study using ML analyses to detect hypertension using population-based surveys. We created a single dataset by harmonizing individual-level data from the most recent nationally representative Demographic and Health Survey in Bangladesh, Nepal, and India. The variables included blood pressure (BP), sociodemographic and economic factors, height, weight, hemoglobin, and random blood glucose. Hypertension was defined based on JNC-7 criteria. We applied six common ML-based classifiers: decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), logistic regression (LR), and linear discriminant analysis (LDA) to predict hypertension and its risk factors. Results Of the 8,18,603 participants, 82,748 (10.11%) had hypertension. ML models showed that significant factors for hypertension were age and BMI. Ever measured BP, education, taking medicine to lower BP, and doctor's perception of high BP was also significant but comparatively lower than age and BMI. XGBoost, GBM, LR, and LDA showed the highest accuracy score of 90%, RF and DT achieved 89 and 83%, respectively, to predict hypertension. DT achieved the precision value of 91%, and the rest performed with 90%. XGBoost, GBM, LR, and LDA achieved a recall value of 100%, RF scored 99%, and DT scored 90%. In F1-score, XGBoost, GBM, LR, and LDA scored 95%, while RF scored 94%, and DT scored 90%. All the algorithms performed with good and small log loss values <6%. Conclusion ML models performed well to predict hypertension and its associated factors in South Asians. When employed on an open-source platform, these models are scalable to millions of people and might help individuals self-screen for hypertension at an early stage. Future studies incorporating biochemical markers are needed to improve the ML algorithms and evaluate them in real life.
Enhanced Passive Stormwater Infiltration Improves Urban Melia Azedarach Functioning in Dry Season

Urban water management projects involving stormwater harvesting, detention, and infiltration are being increasingly combined with urban greening to support adaptation and resilience to the changing climate. A novel stormwater harvesting device, the TREENET Inlet, intercepts stormwater runoff from roads and soaks it into the soil through a leaky well to provide passive irrigation directly into street tree root zones. This study investigated the effects of stormwater harvesting through these inlet systems on the growth, water-use, leaf-level gas exchange and productivity of white cedar (Melia azedarach) street trees in a semi-arid climate in South Australia. The results indicated that mature trees with TREENET Inlets and leaky wells transpired 17% more water per unit of canopy area per day, on average for about a year, and 21% more during the dry season. White cedar saplings with stormwater harvesting grew 65% more in height and 60% more in diameter at breast height over a 3-year period than saplings without stormwater harvesting. This is consistent with observed 106% greater stomatal conductance and up to 169% greater photosynthesis rate in the dry season for saplings supported by harvested stormwater. This study shows that stormwater harvesting and infiltration by TREENET Inlets provides significant benefit to white cedar trees growing in a suburban street.
A Distributed Multi-Agent Formation Control Method Based on Deep Q Learning

Distributed control method plays an important role in the formation of a multi-agent system (MAS), which is the prerequisite for an MAS to complete its missions. However, the lack of considering the collision risk between agents makes many distributed formation control methods lose practicability. In this article, a distributed formation control method that takes collision avoidance into account is proposed. At first, the MAS formation control problem can be divided into pair-wise unit formation problems where each agent moves to the expected position and only needs to avoid one obstacle. Then, a deep Q network (DQN) is applied to model the agent's unit controller for this pair-wise unit formation. The DQN controller is trained by using reshaped reward function and prioritized experience replay. The agents in MAS formation share the same unit DQN controller but get different commands due to various observations. Finally, through the min-max fusion of value functions of the DQN controller, the agent can always respond to the most dangerous avoidance. In this way, we get an easy-to-train multi-agent collision avoidance formation control method. In the end, unit formation simulation and multi-agent formation simulation results are presented to verify our method.
Social, Economic, and Regional Determinants of Mortality in Hospitalized Patients With COVID-19 in Brazil

On May 10, 2021, Brazil ranked second in the world in COVID-19 deaths. Understanding risk factors, or social and ethnic inequality in health care according to a given city population and political or economic weakness is of paramount importance. Brazil had a seriousness COVID-19 outbreak in light of social and economic factors and its complex racial demographics. The objective of this study was to verify the odds of mortality of hospitalized patients during COVID-19 infection based on their economic, social, and epidemiological characteristics. We found that odds of death are greater among patients with comorbidities, neurological (1.99) and renal diseases (1.97), and immunodeficiency disorders (1.69). While the relative income (2.45) indicates that social factors have greater influence on mortality than the comorbidities studied. Patients living in the Northern macro-region of Brazil face greater chance of mortality compared to those in Central-South Brazil. We conclude that, during the studied period, the chances of mortality for COVID-19 in Brazil were more strongly influenced by socioeconomic poverty conditions than by natural comorbidities (neurological, renal, and immunodeficiency disorders), which were also very relevant. Regional factors are relevant in mortality rates given more individuals being vulnerable to poverty conditions.
Comparison of Intestinal Microbiota Between Healthy and MMVD Chihuahuas Using 16S rRNA Gene Amplicon Sequencing

Myxomatous mitral valve disease (MMVD) is the most common cause of congestive heart failure in dogs, and although complications of MMVD to the lungs and kidneys have been identified, complications to the gut are less well understood. The intestinal microbiota is an important factor in the gut, and although the association between heart disease and the intestinal microbiota has been shown in human medicine, it is unknown in dogs. The study aimed to evaluate the relationship between MMVD and gut microbiota. A total of 69 healthy Chihuahuas and Chihuahuas with MMVD were evaluated for cardiac health by echocardiography and chest radiography and grouped according to ACVIM guidelines. Fecal samples were collected from all cases and 16S rRNA sequencing was used to reveal the intestinal microbiota. There were significant differences in LA/Ao, LVIDd, E vel, VHS, and VLAS with the severity of ACVIM. On the other hand, there were no significant differences in the diversity and composition of gut microbiota among the groups. The present study did not identify the effects of MMVD on the gut microbiota.