Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Applications Affecting Students’ Academic Performance: A Model Developed for Sustainability in Higher Education

Nowadays, social media applications (SMAs) which are quite popular among students have a significant influence on education sustainability. However, there is a lack of research that explores elements of the constructivist learning approach with the technology acceptance model (TAM) in higher education. Therefore, this research aimed to minimize the literature gap by examining the SMA factors used for active collaborative learning (ACL) and engagement (EN) to affect the students’ academic performance in measuring education sustainability, as well as examining their satisfaction from its use. This study employed constructivism theory and TAM as the investigation model, and applied a quantitative method and analysis through surveying 192 university students at King Faisal University. Using structural equation modeling (SEM), the responses were sorted into nine factors and analyzed to explain students’ academic performance in measuring education sustainability, as well as their satisfaction. The results were analyzed with structural equation modelling; it was shown that all the hypotheses were supported and positively related to sustainability for education, confirming significant relationships between the use of SMAs and the rest of the variables considered in our model (interactivity with peers (IN-P), interactivity with lecturers (IN-L), ACL, EN, perceived ease of use (PEOU), perceived usefulness (PU), SMA use, student satisfaction (SS), and students’ academic performance (SAP).
Lessons Learned: Utilizing the NEWS Tool in High-Fidelity Simulation

Abstract Nurse educators can enhance students’ clinical judgment by providing cues to guide decision-making during simulation. The purpose of this experimental feasibility study was to evaluate the effectiveness of using the National Early Warning Score as a set of cues during high-fidelity simulation to guide students in the development of clinical judgment, resulting in early detection of patient deterioration. Differences in clinical judgment scores and speed of detection of patient deterioration between groups of junior-level nursing students trained in the use of National Early Warning Score versus those who were not were evaluated. No significant differences were detected between the groups; however, valuable lessons were learned.
Factors related to post-stroke depression during the first 2 weeks after stroke

Introduction This study aimed to clarify factors associated with post-stroke depression in patients with acute stroke within 2 weeks of onset. Method Eighty-eight patients with stroke were divided into post-stroke depression (n = 49) and non-post-stroke depression (n = 39) groups based on their Hamilton Depression Rating Scale scores. We evaluated stroke severity, upper limb function, activities of daily living, frontal lobe/cognitive function, and level of apathy. Activity levels were measured using an ActiGraph GT9X Link system. Mann–Whitney U-tests were used to determine differences between the two groups. Spearman’s rank correlation and logistic regression analyses were also performed. Results The post-stroke depression and non-post-stroke depression groups exhibited significant differences in National Institutes of Health Stroke Scale scores at admission and discharge; Functional Independence Measure exercise items, cognitive items, and total items at admission; Fugl-Meyer Assessment scores; and Apathy Rating Scale scores. Logistic regression indicated that Fugl-Meyer Assessment scores were associated with post-stroke depression. Conclusion Stroke severity, paralysis, and physical function/activities of daily living ability are associated with post-stroke depression in the acute phase. Our findings suggest that increases in physical impairment severity are associated with increased post-stroke depression risks. Rehabilitation professionals should focus on identifying post-stroke depression in early post-stroke stages.
Seedling Growth and Phosphorus Uptake in Response to Different Phosphorus Sources

Phosphorus (P) is an essential element for plant growth and development. Finding new P sources and ways to improve crop P utilization are necessary due to the depletion of phosphate reserves. Five crop species, buckwheat (Fagopyrum esculentum L.), maize (Zea mays L.), oilseed rape (Brassica napus L. ssp. oleifera (Moench) Metzg. ), spelt wheat (Triticum spelta L.), and white lupine (Lupinus albus L.), were grown in pots containing sandy soil with chemical nutrients, digestate, and meat bone meal (MBM) without added nutrients. Thirty days after the seeding plants were harvested, the growth stage, soil-plant analysis development (SPAD) value, biomass, P content of the plants, colonization of the roots with endomycorrhiza, and soil pH were analyzed, and the number of fungal spores in the soil was counted. All species showed interaction with the P sources for measured traits, except for the rhizosphere pH. A high biomass was recorded in all species fertilized with various P sources compared to the unfertilized treatment. Buckwheat and spelt wheat showed a higher P uptake with MBM, and the mycorrhizal symbiosis improved with digestate or MBM compared to synthetic P. The results indicate that different species have adaptative mechanisms to various P sources which could improve the resilience and sustainability of cropping systems.
Effect of Remediation Activities on Grade 5 Pupils’ Academic Performance in Technology and Livelihood Education (TLE)

This study assesses the effectiveness of remediation activities of Grade 5 pupil’s academic performance in the subject Technology and Livelihood Education (TLE). The study used an experimental pretest-posttest design. We chose two schools in the experiment and each school has a class with 50 pupils each. We assigned one school as the independent group and the other one was the experimental group. The researchers worked with the respective teachers in TLE to determine the level of academic performance of the students. First, we administered a pretest during their first grading period. The results for both schools were not impressive, prompting the researchers to advise remediation activities to the experimental group. We advised the independent group to do the usual learning methods. After the intervention, the researcher performed another set of assessment using the results of the second grading period performance. The researchers used a paired t-test to check for significant differences in the scores of the groups. The study found that there is a significant difference in the scores of the pupils after the remediation intervention. Therefore, the researchers concluded that remediation activities can affect the academic performance of the students.
Environmental Sustainability of Plastic in Agriculture

This article investigates the environmental sustainability of plastic nets in agricultural environments based on published experimental data. This article focuses on biodegradable and synthetic plastics used in farms as mulching materials and shade materials/greenhouse covering materials (shade nets and plastic films) to protect plants from pests and extreme weather. The sustainability was determined by three factors, carbon footprint from cradle to the end of life (LCA), durability (resistance to photo-oxidation and high tensile strength), and affordability. The LCA analyses showed that the production of polyethylene (PE) requires less energy and generates low quantities of greenhouse gas equivalents. Beyond the LCA data, biodegradable polymers are sustainable based on biodegradability and compostability, ability to suppress weeds, control soil temperatures, and moisture, and augment fertigation and drip irrigation. However, existing technologies are a limiting factor because lab-based innovations have not been commercialized. In addition, industrial production of shade nets, plastic greenhouse covers, and mulching materials are limited to synthetic plastics. The bio-based plastic materials are sustainable based on biodegradability, and resistant to photo-oxidation. The resistance to UV degradation is an essential property because solar radiation cleaves C-C bonds, which in turn impact the mechanical strength of the materials. In brief, the sustainability of plastics in farms is influenced by LCA data, mechanical and optical properties, and performance relative to other materials.
Investigation of the Internal Tides in the Northwest Pacific Ocean Considering the Background Circulation and Stratification

A global ocean circulation and tide model with nonuniform resolution is used in this work to resolve the ocean circulation globally as well as mesoscale eddies and internal tides regionally. Focusing on the northwest Pacific Ocean (NWP, 0°–35°N, 105°–150°E), a realistic experiment is conducted to simulate internal tides considering the background circulation and stratification. To investigate the influence of a background field on the generation and propagation of internal tides, idealized cases with horizontally homogeneous stratification and zero surface fluxes are also implemented for comparison. By comparing the realistic cases with idealized ones, the astronomical tidal forcing is found to be the dominant factor influencing the internal tide conversion rate magnitude, whereas the stratification acts as a secondary factor. However, stratification deviations in different areas can lead to an error exceeding 30% in the local internal tide energy conversion rate, indicating the necessity of a realistic stratification setting for simulating the entire NWP. The background shear is found to refract propagating diurnal internal tides by changing the effective Coriolis frequencies and phase speeds, while the Doppler-shifting effect is remarkable for introducing biases to semidiurnal results. In addition, nonlinear baroclinic tide energy equations considering the background circulation and stratification are derived and diagnosed in this work. The mean flow–baroclinic tide interaction and nonlinear energy flux are the most significant nonlinear terms in the derived equations, and nonlinearity is estimated to contribute approximately 5% of the total internal tide energy in the greater Luzon Strait area.
Application of genomics and transcriptomics to accelerate development of clubroot resistant canola

Abstract Clubroot, a soil-borne disease caused by the obligate parasite Plasmodiophora brassicae Woronin, is a threat to canola (Brassica napus L.) production in western Canada. Genetic resistance represents the most effective tool to manage this disease. However, given the rapid spread of clubroot and the emergence of new virulent pathotypes of P. brassicae, it is important to accelerate the resistance breeding effort. The advent of genome-sequencing technologies has created a new toolbox that can aid in breeding strategies. Many genomic approaches, such as genotyping-by-sequencing, high-density single nucleotide polymorphism (SNP) arrays, genome-wide association studies, and transcriptomic approaches such as bulked segregant RNA-seq analysis and microarray/RNA-seq differential expression analysis have been applied to studies of clubroot resistance or susceptibility. Here, we review the impact of traditional marker-assisted selection-based breeding for clubroot resistance, and then discuss how omics approaches have contributed to the (1) detection and genotyping of genome-wide SNP markers linked with clubroot resistance genes or quantitative trait loci, (2) understanding of host resistance mechanisms upon P. brassicae infection, and (3) acceleration of resistance breeding by identifying and characterizing candidate genes, especially those with differential efficacy against new pathotypes of P. brassicae. We suggest that the use of different omics approaches together could improve the efficiency of clubroot-resistance breeding. Finally, we propose that the CRISPR/Cas9 system for genome editing is a promising tool to facilitate the validation and use of candidate genes for clubroot-resistance breeding.
Evaluation of biologic and non-biologic methods for assessing virus removal by and integrity of high pressure membrane systems

This paper describes pilot-scale studies that examined three integrity test methods for: (1) quantifying virus removal by nanofiltration (NF) and reverse osmosis (RO) membrane systems when arranged in single element unit and two-stage system configurations, and (2) determining change in virus removal capability of such systems when subject to different types of membrane/o-ring compromisation and fouling. The three methods evaluated included one biologic type (MS-2 phage), that has been employed previously; and two, new non-biologic types (24-nanometre polystyrene fluorescent dyed microspheres and fluorescent Rhodamine WT [R-WT] dye, molecular mass 496 daltons). All three surrogates were employed in a manner intended to show a minimum of 4-logs removal by the NF and RO membranes selected for test. Methods of compromisation included a pinhole induced through one membrane leaf in the spiral wound NF/RO element, and both cracking of and removal of sections from one of the permeate tube o-rings. Testing was conducted on two source waters, representing brackish surface water and effluent categories: a microfiltered secondary effluent and a river water. The river water is characterized by low to moderate TDS and high TOC and was treated with conventional alum coagulation, flocculation, sedimentation and granular media filtration for subsequent membrane processing.
Vitamin D Receptor Gene Polymorphism: An Important Predictor of Arthritis Development

Vitamin D is an anti-inflammatory molecule and has a role in prevention of arthritis development. Biologically active form 1, 25(OH)2D3 of vitamin D can only exert its action after binding its definite vitamin D receptor encoded by VDR gene. VDR gene polymorphism leads to dysfunctioning of 1, 25(OH)2D3 ultimately disease onset. The purpose of current study was to evaluate the effect of vitamin D level and VDR gene polymorphism on rheumatoid arthritis and osteoarthritis. Blood samples were collected from case and control after taking written consent. Serum was separated and vitamin D level as determined from each sample by ELISA. DNA was extracted from each blood sample and amplified by using gene specific primers. Genotyping was performed by Sangers sequencing and PCR-RFLP technique. It was found that vitamin D level was not significantly different among patients and controls. The rs10735810, rs1544410, rs7975232, and rs731236 were associated with the onset of arthritis at both allelic and genotypic level (p < 0.01). Nucleotide change on rs10735810 site leads to change of tryptophan with arginine. The frequencies of haplotype CGAT, CGGA, CGGT, CTAA, CTAT, TGAA, TGAT, TGGA, and TTGA were higher in patients and act as risk factors of RA onset, whereas haplotypes CGAT, CGAT, CGGT, CTGA, TGAT, TGGA, TTAA, and TTGA were associated with OA onset. In conclusion, serum vitamin D level may be normal among arthritis patients but polymorphism on VDR gene restricts vitamin D to perform its anti-inflammatory function by altering the 1, 25(OH)2 D3 binding sites.
Cytokinin-Regulated Physiological Parameters Affected by an Exogenous Dopamine Spray in Brussels Sprout (Brassica oleracea var. gemmifera)

Root restriction on the first stage of seedling growth decreases post-transplant biomass accumulation. Several studies in different vegetables and ornamental plants have suggested that this restriction would be overcome by means of a single 6-benzylaminopurine (BAP) spray. Based on this, the aim of this work was to evaluate the effects of both pre-transplant single BAP and dopamine (a cytokinin antagonist) sprays on the growth of Brussels sprout (Brassica oleracea var. gemmifera) plants grown in 200 plug cells tray -1 , during different times of the annual cropping period. The responses of dopamine-sprayed plants were not completely similar to those previously reported. The different physiological processes studied in dopamine-sprayed plants through some growth parameters showed that dopamine affected mainly the rate of leaf appearance, which in turn positively affected both individual and total leaf area expansion. Dopamine-sprayed plants showed a higher leaf source, which allowed them to accumulate a higher biomass on both a fresh and dry weight basis during the initial vegetative growth stages. The effects of both BAP and dopamine were partially related to the environmental conditions during the experiments. Original Research Article Miglioli et al.; AJAHR, 6(3): 24-36, 2020; Article no.AJAHR. 59456 25
Built environment correlates of overweight and obesity among adults in Chennai, India

ABSTRACT Overweight and obesity are steadily rising in South Asia, the world’s most populous region. India, a low- and middle-income country in South Asia is experiencing an obesity epidemic. Built environment features may profoundly influence physical activity and counter the risk from overweight and obesity. This cross-sectional study investigated built environment correlates of overweight and obesity among adults (N = 370, 47.2% female, mean age = 37.9 years) in Chennai, India. Participants from low-socioeconomic status households were more likely to be overweight/obese (odds ratio [OR] = 1.8, 95% confidence interval [CI] = 1.1–2.9) than participants from high-socioeconomic status households. Driving to work predicted a three-fold increase in overweight or obesity compared to active commuting (OR = 2.9, 95%CI = 1.3–6.4). Household car ownership was linked with an increased likelihood of overweight or obesity (OR = 1.5, 95%CI = 0.7–3.2). Low walkability neighbourhoods significantly predicted an increase in odds of overweight or obesity (OR = 1.8, 95%CI = 1.1–2.8). By 2030, Indian cities are projected to add 250 million people accompanied by a 9.9% annual motor vehicle growth rate. As car ownership increases across Southern Asia, there is an urgent need to develop interventions to promote physical activity.
Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been proposed based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (‘out-of-sample’) has yet been demonstrated. Here, we present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell type and species specific response implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.
Financial shocks to banks, R&D investment, and recessions

In some classes of macroeconomic models with financial frictions, an adverse financial shock successfully explains a decrease in real activity but simultaneously induces a stock price boom. The latter theoretical result is not consistent with data from actual financial crises. This study aims to provide a theoretical explanation for both prolonged recessions and stock price declines. I develop a simple macroeconomic model featuring a banking sector, financial frictions, and R&D-based endogenous growth. Both the analytical and numerical investigations show that endogenous R&D investment and a shock hindering banks’ financial intermediary function can be key to generating both a prolonged recession and a drop in firms’ stock prices.
Effect of Mold Vibration on Microstructure and Mechanical Properties of Al-Si Eutectic Alloy

Aluminum-silicon foundry alloys at eutectic region have small range of freezing, good castability and good properties. Such alloys are used in many engineering applications like automobile, aeronautical and aerospace industries. In present work, aluminum-silicon eutectic samples with silicon of 12-13wt. % were prepared using stir casting method. The microstructure, hardness, porosity, dendrite grain size, tensile strength, and elongation were studied to see the effect of mechanical mold vibration on this eutectic alloy. An amplitude of 0.5 mm and frequency of 5,10,15,20 and 25 Hz were used for casting the samples with mechanical mold vibration. The results showed that the values of the amplitude and frequencies have significant effect on the studied properties. A decrease in dendrite grain size by used mechanical mold vibration, at frequencies(5,10,15,20 and 25)Hz the decreased by 15%, 26%, 32%, 42%, and 53%. The hardness was increased by used mechanical mold vibration, at frequencies(5,10,15,20 and 25)Hz the hardness increased by 7%, 16%, 25%, 33%, and 40%. Also, a decrease in porosity by used mechanical mold vibration, at frequencies(5,10,15,20 and 25)Hz the porosity decreased by 35%, 46%, 58%, 69%, and 77%. The increase in tensile strength by used mechanical mold vibration, at frequencies(5,10,15,20 and 25)Hz the increased by 12%, 18%, 25%, 29%, and 36%; and increase in elongation percentage by used mechanical mold vibration, at frequencies(5,10,15,20 and 25)Hz the increased by 14%, 29%, 38%, 52% and 71% respectively.
Survey on Recommender System Using Deep Learning Networks

In today’s times, the recommended system is a very powerful weapon of shoppers that is very helpful in advancing the Internet, personalized tendencies, and online shopping. The recommended system is used primarily for commercial benefit. The recommended system works on the strength of the user’s past shopping experience and its feedback, whether it is positive or negative. Hence the recommended system is also an innovative method. There is a deferred method of the recommended system which has its own advantages and disadvantages. In this paper, the Recommender system based on deep learning is proposed, and also discussed the challenges and issues which are related to the deep learning based recommender system. i.e., Accuracy, Cold Start Problem, Scalability States etc. In this paper, we have also discussed the work done so far, which has been given by various scientists / researchers / investigators. Advancement of machine learning and deep learning is very big, in today’s era. This study will help the Researcher to move forward.
UV Spectroscopy of Massive Stars

We present a review of UV observations of massive stars and their analysis. We discuss O stars, luminous blue variables, and Wolf–Rayet stars. Because of their effective temperature, the UV (912−3200 Å) provides invaluable diagnostics not available at other wavebands. Enormous progress has been made in interpreting and analysing UV data, but much work remains. To facilitate the review, we provide a brief discussion on the structure of stellar winds, and on the different techniques used to model and interpret UV spectra. We discuss several important results that have arisen from UV studies including weak-wind stars and the importance of clumping and porosity. We also discuss errors in determining wind terminal velocities and mass-loss rates.