Fast-Tracking Isolation, Identification and Characterization of New Microalgae for Nutraceutical and Feed Applications

Microalgae contain high-value biochemical compounds including fatty acids (FA), protein and carotenoids, and are promising bioresources to enhance nutrition of food and animal feed. Important requirements for commercial strains are rapid growth and high productivities of desirable compounds. As these traits are believed to be found in aquatic environments with fluctuating conditions, we collected microalgae from marine and freshwater environments that are subjected to eutrophication and/or tidal fluctuations. Using this directed approach, 40 monoalgal cultures were isolated and 25 identified through 18S rDNA sequencing and morphological characterization. Based on their high growth rates (0.28–0.60 day−1) and biomass productivities (0.25–0.44 g L−1day−1) in commercial fertilizer under standardized conditions, six new strains were selected. Scenedesmus sp. GW63 produced quality FA-rich biomass with high omega-3 polyunsaturated FA (28.5% of total FA (TFA)) contents, especially α-linolenic acid (ALA; 20.0% of TFA) with a very low n-6/n-3 ratio (0.4), and high FA productivity (32.6 mg L−1 day−1). A high protein productivity (34.5 mg L−1 day−1) made Desmodesmus sp. UQL1_26 (33.4% of dry weight (DW)) attractive as potential protein-rich feed and nutrition supplement. Monoraphidium convolutum GW5 displayed valuable carotenoid production (0.8% DW) with high carotenoid accumulation capability (0.8 mg L−1 day−1). This research provides a pathway for fast-tracking the selection of high-performing local microalgae from different environments for nutraceuticals, functional foods and animal feed applications.

Household Possessions and Parental Support in Mexican Students with High Scientific Competencies in PISA 2015

<p style="text-align:justify">Traditionally secondary studies on achievement on Programme for International Students Assessment (PISA) tests point to the significant impact of socioeconomic status and cultural backgrounds of families as well as the role of parental involvement, which in some cases has had a negative impact on achievement. For this article, a model of structural regression was tested, with structural modelling software. This model included the following factors: domestic and educational assets, parental support for students, parents’ perceptions about science, and science competencies among 214 high performing Mexican students on PISA tests in 2015. This resulted in a structural regression model with a goodness of fit, where science competencies were a positive significant variable, impacted by domestic and educational assets and parental involvement. An additional restricted model with four variables manifested as mediators, revealed that science competencies were predicted positively and significantly by domestic and educational assets, and by the manifest parental emotional support variable. Variables related to ownership of educational and cultural assets and resources, as well as parental support, particularly emotional parental support, have positive and significant impact on science competencies.</p>

The effects of course format, sex, semester, and institution on student performance in an undergraduate animal science course

The transition of courses from in-person to an online format due to the COVID-19 pandemic could have potentially affected overall student performance in lecture-based courses. The objective of this case study was to determine the impact of course format, as well as the effects of student sex, time of year at which the course was taken, and the institution it was taken at on student performance in an undergraduate animal science course. The course used for this study was taught at two institutions (University of Florida; UF and University of Nevada, Reno; UNR) over seven years (2014-2017 at UNR and 2018-2021 at UF). Student performance (n = 911) was evaluated using both quizzes and exams from 2014 through the spring semester 2020 and only exams were used for summer and fall semesters of 2020 and the spring and summer semesters of 2021. The final score (out of 100%) for each student was used to evaluate student performance. In addition, students were classified as high performing students if they scored ≥ 95% and low performing students if they scored ≤ 70%. The variables that were evaluated were the effects of semester (spring, summer, or fall), institution (UF or UNR), sex (male or female), number of teaching assistants (TAs; 0 to 13), and course format (online or in-person). The course was taught in-person at UNR and in-person and online at UF. The spring semester of 2020 was taught in-person until March but was switched to online approximately nine weeks after the semester started and was considered an online semester for this analysis. As the course was only taught online at UF, the variable course format was assessed using UF records only. Data was analyzed using both linear models and logistic regressions. The probability that students were high performing was not affected by sex or institution. Interestingly, both fall semester, and the online format had a positive, desirable effect on the probability that students were high performing. The probability that students were low performing was not affected by sex. However, if a student performed poorly in the class, they were more likely to have taken the course at UNR, or at UF with many TAs. Thus, student performance was impacted by changing the course format, as well as institution, the number of TAs, and the semester in which the course was taken.

Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior.

Revitalising Jiri Technical School in a Dramatically Changed Context: Governance, Management and Employability

This paper examines two assumption: First, whether inadequate practical training, including much shortened apprenticeship training, has negatively affected the employability and incomes of graduates of TVET institutions in Nepal, and the second, whether it is good institutional management and governance that provide the systems for quality training and positive labour market outcomes. Tracer studies and an institutional assessment of Jiri Technical School (JTS) confirm the first assumption. The review of select literature on institution building and the benchmarking of JTS’ operative practices against those of high performing educational institutions (in India) confirm the second assumption. It is argued that poor management and governance of TVET institutions drifts the mission of Technical Schools away from their initial socio-economic mandate: the provision of skilled human resource and access to qualification opportunities to the youth having the aptitudes for such an education. Social rather than labour market demand with corresponding politics is one major force for such deviation. To revitalise the JTS, it is proposed to bank on the federalisation of the TVET governance system to professionalise Board, Management and teachers for enhanced labour market outcomes in closer cooperation between actors from the education and employment systems.