Miracle-dz/UniFAR-FTUs huggingface dataset:
Does it Matter Where You Teach? Insights from a Quasi-Experimental Study on Student Engagement in an Active Learning Classroom

Active learning has experienced a recent resurgence with the advent of specialized active learning classrooms. While the fundamental theory behind active learning is anything but new, a relatively recent finding is that active learning pedagogies thrive in suitable active learning classrooms. To date, studies of active learning have focused on outcomes such as student performance. The quasi-experimental study described in this article investigated selfratings of student engagement as an outcome of active learning in active learning classrooms using a novel instrument that accounts for known factors of engagement in addition to the contribution of the learning environment—the classroom. We delineated the relative contributions of instructor, classmates, and classroom to self-rated student engagement through student surveys in both a traditional classroom and an active learning classroom in two highly similar courses with the same instructor. Our findings were that the configuration of the classroom had a direct influence on self-ratings of student engagement above and beyond instructor contributions. In this article, we describe these findings and how, with careful consideration of course design and a classroom that fits the instructor’s pedagogy, optimal levels of perceived student engagement can be achieved. This knowledge is important to future educational policy on construction and scheduling, as the resurgence of active learning in higher education increasingly reveals deficiencies in physical learning environments.
Description of a new branchiate scale-worm (Polychaeta: Polynoidae) from the hydrothermal vent on Southwest Indian Ocean Ridge

During the Dive90 and Dive94 of Chinese manned submersible “Jiaolong”, some polychaete specimens were collected from the “Longqi” vent field on the Southwest Indian Ocean Ridge. A new species belonging to the genus Branchipolynoe , Branchipolynoe longqiensis n. sp. , is described in this study. It can be easily distinguished from congeners by its biramous parapodia with relatively reduced notopodia, stout notosetae and branchiae with short terminal filaments.
Antibacterial Activity of Bacterial Endophytes from Kupa Plant (Syzygium Polycephalum Miq. (Merr & Perry) Against Pathogenic Bacteria

The World Health Organization confirms that infectious diseases are normally treated using synthetic antibiotics. The bacterial endophytes from the tropical plant Syzygium polycephalum, locally known as Kupa, can be used as an alternative solution to reduce the utilization of synthetic antibiotics. The purpose of this study was to obtain one of the most potent bacterial endophytes to be used as antibacterial agents. In the present study, descriptive analysis was conducted consisting of bacterial isolation by pour plate method, bacterial identification with Vitek 2 compact BIOMERIEUX and antibacterial test by Kirby-bauer method. A total of 9 isolates of bacterial endophytes have been successfully obtained. From these isolates, a total of 4 species of endophytic bacteria were identified: Bacillus sp. (1), Bacillus sp. (2), Bacillus pumilus and Bacillus amyloliquefaciens. Our antibacterial tests revealed that Bacillus sp. (2) derived from the leaves appeared to be the most potent antibacterial isolates against pathogenic bacteria with 22 and 9 mm of inhibitory zone to Methicillin-resistant Staphylococcus aureus (MRSA) and to Bacilluscereus, respectively. On the other hand, endophytic isolate Bacillus sp (1) derived from stem was able to inhibit Klebsiellapneumoniae and B. cereus with inhibitory zones as much as 10 and 7 mm, respectively. This result strongly indicated that the antibacterial effect of bacterial endophytes from this study was species-specific and indeed the bacterial endophytes in this study could serve as a potential source of novel natural antibiotics.
Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Abstract. Despite showing a great success of applications in many commercial fields, machine learning and data science models in general, show a limited use in scientific fields including hydrology. The approach is often criticized for lack of interpretability and physical consistency. This has led to the emergence of new paradigms, such as Theory Guided Data Science (TGDS) and physics informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles, in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on Genetic Programming (GP) namely Machine Learning Rainfall-Runoff Model Induction Toolkit (ML-RR-MI). ML-RR-MI is cable of developing fully-fledged lumped conceptual rainfall-runoff models for a watershed of interest using the building blocks of two flexible rainfall-runoff modelling frameworks (FUSE and SUPERFLEX). In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall-runoff models. This effort is motivated by the desire to address the decreasing meaningfulness of lumped models which tend to particularly deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties are significant. Henceforth, our machine learning approach for rainfall-runoff modelling titled Machine Induction Knowledge-Augmented System Hydrologique Asiatique (MIKA-SHA) captures spatial variabilities and automatically induces rainfall-runoff models for the catchment of interest without any subjectivity in model selection. Currently, MIKA-SHA learns models utilizing the model building components of FUSE and SUPERFLEX. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA’s model induction capabilities have been tested on the Red Creek catchment near Vestry, Mississippi, United States. The resulted model architectures through MIKA-SHA are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists.
USING GENOMICS TO UNDERSTAND POPULATION DEMOGRAPHICS IN THE CONTEXT OF AMPHIBIAN CONSERVATION

OF DISSERTATION USING GENOMICS TO UNDERSTAND POPULATION DEMOGRAPHICS IN THE CONTEXT OF AMPHIBIAN CONSERVATION Understanding the demography of species over recent history (e.g., < 100 years) is critical in studies of ecology and evolution, but records of population history are rarely available. Large single nucleotide polymorphism datasets generated with restriction-site associated DNA sequencing (RADseq), in combination with demographic inference methods, are improving our ability to gain insights into the population history of both model and non-model species. However, to assess the performance of genetic methods it is important to compare their estimates of population history to known demography, in both simulation and empirical settings. Here, I used a simulation approach to examine the potential for RADseq datasets to accurately estimate effective population size (Ne) in Wright-Fisher populations over the course of stable and declining population trends, and distinguish stable from steadily declining populations over a contemporary time scale (20 generations). Overall, my results reveal that demographic inference using genome-wide data can be successfully applied to estimate Ne, and the detection of population-size declines. Next, I assess these methods in an empirical study from a wetland with 37 years of amphibian mark-recapture data to study the utility of genetically-based demographic inference on salamander species with documented population declines (Ambystoma talpoideum) and expansions (A. opacum). For both species, demographic model inference supported population size changes that corroborated mark-recapture data. To further validate these findings, I used individual-based population models of the pond-breeding salamander, Ambystoma opacum, with life-history parameters estimated from a long-term dataset, over a 50 year projection. My results demonstrate that genetically estimated Ne is positively correlated with census size in isolated and subdivided A. opacum populations. Finally, I investigated metapopulation patterns of genomic diversity in A. opacum and A. talpoideum and how migration may impact Ne estimation. I found strong patterns of subpopulation structuring, signatures of migration between subpopulations, and differences in Ne at the subpopulation level in both species. Overall, my findings suggest the ability of genomic data to reconstruct recent demographic changes, which can have important applications to conservation biology, and ultimately can help us elucidate the effects of environmental disturbances in the demography of endangered or declining species.
Life between buildings from a street view image: What do big data analytics reveal about neighbourhood organisational vitality?

This article uses big data from images captured by Google Street View (GSV) to analyse the extent to which the built environment impacts the survival rate of neighbourhood-based social organisations in Amsterdam, the Netherlands. These organisations are important building blocks for social life in urban neighbourhoods. Examining these organisations’ relationships with their environment has been a useful way to study their vitality. To extract data on built environment features from GSV images, we applied a deep learning model, DeepLabv3+. We then used elastic net regression to test the relationship between the built environment empirically – distinguishing between car-related, walking-related and mixed-use land infrastructure – and the survival of neighbourhood organisations. This testing approach is novel, to our knowledge not yet having been applied in Urban Studies. Besides revealing the effects of built environment features on the social life between buildings, our study points to the value of easily applicable observational big data. Data captured by GSV and other recently developed methods offer researchers the opportunity to conduct detailed yet relatively swift and inexpensive studies without resorting to overly coarse or common subjective measurements.
A Pragmatic Machine Learning Model To Predict Carbapenem Resistance

Infection caused by carbapenem-resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are well known, there remains a large need for the early identification of antibiotic-resistant infections. ABSTRACT Infection caused by carbapenem-resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are well known, there remains a large need for the early identification of antibiotic-resistant infections. Using machine learning (ML), we sought to develop a prediction model for carbapenem resistance. All patients >18 years of age admitted to a tertiary-care academic medical center between 1 January 2012 and 10 October 2017 with ≥1 bacterial culture were eligible for inclusion. All demographic, medication, vital sign, procedure, laboratory, and culture/sensitivity data were extracted from the electronic health record. Organisms were considered CR if a single isolate was reported as intermediate or resistant. Patients with CR and non-CR organisms were temporally matched to maintain the positive/negative case ratio. Extreme gradient boosting was used for model development. In total, 68,472 patients met inclusion criteria, with 1,088 patients identified as having CR organisms. Sixty-seven features were used for predictive modeling. The most important features were number of prior antibiotic days, recent central venous catheter placement, and inpatient surgery. After model training, the area under the receiver operating characteristic curve was 0.846. The sensitivity of the model was 30%, with a positive predictive value (PPV) of 30% and a negative predictive value of 99%. Using readily available clinical data, we were able to create a ML model capable of predicting CR infections at the time of culture collection with a high PPV.
Lower Hybrid Waves at the Magnetosheath Separatrix Region

Lower hybrid waves are investigated at the magnetosheath separatrix region in asymmetric guide field reconnection by using the Magnetospheric Multiscale (MMS) mission. Three of the four MMS spacecraft observe clear wave activities around the lower hybrid frequency across the magnetosheath separatrix, where a density gradient is present. The observed waves are consistent with generation by the lower hybrid drift instability. The characteristic properties of these waves include the following: (1) the waves propagate toward the x‐line in the spacecraft frame due to the large out‐of‐plane magnetic field, which is in the same direction of the diamagnetic drift of the x‐line; (2) the wave potential is about 20% of the electron temperature. These drift waves effectively produce cross‐field particle diffusion, enabling the transport of magnetosheath electrons into the exhaust region. At last, we suggest that the lower hybrid waves at the magnetosheath separatrix region represent some unique features of asymmetric guide field reconnection, which is different from that widely observed at the magnetospheric side of magnetopause reconnection.
Testing Metal–Organic Framework Catalysts in a Microreactor for Ethyl Paraoxon Hydrolysis

We explored the practical advantages and limitations of applying a UiO-66-based metal–organic framework (MOF) catalyst in a flow microreactor demonstrated by the catalytic hydrolysis of ethyl paraoxon, an organophosphorus chemical agent. The influences of the following factors on the reaction yield were investigated: a) catalyst properties such as crystal size (14, 200, and 540 nm), functionality (NH2 group), and particle size, and b) process conditions: temperature (20, 40, and 60 °C), space times, and concentration of the substrate. In addition, long-term catalyst stability was tested with an 18 h continuous run. We found that tableting and sieving is a viable method to obtain MOF particles of a suitable size to be successfully screened under flow conditions in a microreactor. This method was used successfully to study the effects of crystal size, functionality, temperature, reagent concentration, and residence time. Catalyst particles with a sieved fraction between 125 and 250 µm were found to be optimal. A smaller sieved fraction size showed a major limitation due to the very high pressure drop. The low apparent activation energy indicated that internal mass transfer may exist. A dedicated separate study is required to assess the impact of pore diffusion and site accessibility.
Exploiting the full potential of Bayesian networks in predictive ecology

Although ecological models used to make predictions from underlying covariates have a record of success, they also suffer from limitations. They are typically unable to make predictions when the value of one or more covariates is missing during the testing. Missing values can be estimated but methods are often unreliable and can result in poor accuracy. Similarly, missing values during the training can hinder parameter estimation of many ecological models. Bayesian networks can handle these and other limiting issues, such as having highly correlated covariates. However, they are rarely used to their full potential. Indeed, Bayesian networks are commonly used to evaluate the knowledge of experts by constructing the network manually and often (incorrectly) interpreting the resulting network causally. We provide an approach to learn a Bayesian network fully from observed data, without relying on experts and show how to appropriately interpret the resulting network, both to identify how the variables (covariates and target) are interrelated and to answer probabilistic queries. We apply this method to the case study of a mountain pine beetle infestation and find that the trained Bayesian network has a predictive accuracy of 0.88 AUC. We classify the covariates as primary and secondary in terms of contributing to the prediction and show that the predictive accuracy does not deteriorate when the secondary covariates are missing and degrades to only 0.76 when one of the primary covariates is missing. As a complement to the previous work on constructing Bayesian networks by hand, we show that if instead, both the structure and parameters are learned only from data, we can achieve more accurate predictions as well as generate new insights about the underlying processes.
GENERATION OF DNA BARCODE DATABASE FOR CARDINALFISHES OF TAMIL NADU COAST

Title : Generation of DNA barcode database for Cardinalfishes of Tamil Nadu Coast Name of the student : R. RAJESHKANNAN Degree : M.F. Sc. , (Fish Biotechnology) Chairman : Dr. J. Jaculine Pereira Department : Department of Fisheries Biotechnology College : Fisheries College and Research Institute, Thoothukudi. Faculty : Faculty of Basic Sciences Year and University : 2017, Tamil Nadu Fisheries University, Nagapattinam. Cardinalfishes belongs to the family, Apogonidae is cryptic in nature that often shows taxonomic ambiguity through conventional taxonomy. It is globally accepted that mitochondrial DNA marker i.e., Cytochrome C Oxidase (COI) can be used to resolve these taxonomic uncertainties. In the present study, the DNA barcode was developed using COI marker for the two species of cardinalfishes (Archamia bleekeri and Ostorhinchus fleurieu) collected from Thoothukudi coast. Results showed that the distance values between the two species are higher than that of within the species. The Cytochrome C Oxidase subunit I (COI) gene showed more number of transitional pairs (Si) than transversional pairs (Sv) with a ratio of 2.4. The average distance values between A. bleekeri and O. fleurieu were 7.39, 8.14, 7.18 and distance values among the A. bleekeri and O. fleurieu were 4.83 and 4.24, 7.42, 3.72 respectively using K2P parameter. The average nucleotide frequency calculated were A= 26.9%, T(U)= 24.3%, C= 21.95% and G= 26.8%. The estimated GC content of A. bleekeri and O. fleurieu was (49.9%) and (47.6%) respectively and the average GC content was found to be 48.75%. Phylogenic trees were constructed individually for the two species using MEGA 6.0 software and the Neighbour-Joining tree showed distinct clusters shared by the species of same genera. In conclusion, the present study developed DNA barcode database for the two species of cardinal fishes that can be used for taxonomic purposes for these species.
Electronic Module With Project Based Learning: Innovation of Digital Learning Product on 4.0 Era

Digital teaching materials become one of the main needs in the field of education in this 4.0 era. Digital teaching materials continue to be innovated in various forms such as e-modules in learning. Based on this need, this study aims to develop e-modules that are collaborated with PjBL for learning basic skills of learning. The study was conducted with a development research procedure that adapted the Dick & Carey development model (2015). Development research is carried out with a validation test to the expert and user test to obtain data on the results of the application of the product as appropriate teaching material. The results of the product development show that the collaboration between e-modules and PjBL received a good response from experts and users both educators and students who met the qualifications of appropriate criteria as teaching material. This development is one form of digital product innovation that bridges current and future learning needs for students to skillfully use IT in the digital age in learning as prospective educator.
Symbiodinium functional diversity and clade specificity under global change stressors

Coral bleaching episodes are increasing in frequency, demanding examination of the physiological and molecular responses of corals and their Symbiodinium to climate change. Here we quantify bleaching and Symbiodinium photosynthetic performance of Siderastrea siderea from two reef zones after long-term exposure to thermal and CO2-acidification stress. Molecular response of in hospite Symbiodinium to these stressors was interrogated with RNAseq. Elevated temperatures reduced photosynthetic efficiency, which was highly correlated with bleaching status. However, photosynthetic efficiencies of forereef symbionts were more negatively affected by thermal stress than nearshore symbionts, indicating greater thermal tolerance in nearshore corals. At control temperatures, CO2-acidification had little effect on symbiont physiology, although forereef symbionts exhibited greater photosynthetic efficiencies than nearshore symbionts. Transcriptome profiling revealed that S. siderea were dominated by clade C Symbiodinium, except under thermal stress, which caused shifts to thermotolerant clade D. Comparative transcriptomics of conserved genes across symbiotic partners revealed few differentially expressed Symbiodinium genes when compared to corals. Instead of responding to stress, clade C transcriptomes varied by reef zone, with forereef Symbiodinium exhibiting enrichment of genes associated with photosynthesis. Our findings suggest that functional variation in photosynthetic architecture exists between forereef and nearshore Symbiodinium populations.
Thickness of Lava Flows Within the Northern Smooth Plains on Mercury as Estimated by Partially Buried Craters

Partially and completely buried impact craters are widely distributed over the northern smooth plains of Mercury. Using imagery data acquired from the MESSENGER mission, we compiled a database of 257 partially buried and 765 completely buried craters on Mercury. The elevation profiles of fresh craters were constructed, and by inputting these into a topographic degradation model, we estimated the lava flow thicknesses around partially buried craters. Along the edges of the northern smooth plains, lava flow thicknesses were inverted to be 23–536 m with a median of 228 m. Lava flow thicknesses on Mercury are twice that on the Moon, possibly due to a denser crust on Mercury that favors the ascent of magmas or to higher production rates of magmas. The topographic diffusivity used for crater degradation on Mercury is 3 times that for lunar craters, which is likely a result of a higher cratering rate on Mercury.
Oxygen Transfer Capacity as a Measure of Water Aeration by Floating Reed Plants: Initial Laboratory Studies

Reed-Phragmites australis (Cav.) Trin. ex Steud, an aquatic plant, commonly used in constructed wetlands for wastewater treatment, supplies oxygen into the subsurface environment. Reed may be used as a ‘green machine’ in the form of a floating vegetation cover with many applications: wastewater lagoons, manure lagoons or sewage sludge lagoons. An important measure of the performance of the plant system is the oxygen transfer capacity (OTC). Accurate prediction of the OTC in relation to reed biomass would be crucial in modelling its influence on organic matter degradation and ammonia–nitrogen oxygenation in such lagoons. Laboratory experiments aiming to determine OTC and its dependence on reed biomass were carried out. Eight plants with a total dry mass ranging from approximately 3 to 7 g were tested. Mean OTC was determined per plant: 0.18 ± 0.21 (g O2·m−3·h−1·plant−1), with respect to leaves-and-stem dry mass (dlsm): 44.91 ± 35.21 (g O2·m−3·h−1·g dlsm−1), and to total dry mass (dtm): 33.25 ± 27.97 (g O2·m−3·h−1·g dtm−1). In relation to the relatively small root dry mass (drm), the OTC value was 136.02 ± 147.19 (g O2·m−3·h−1·g drm−1). Measured OTC values varied widely between the individual plants (variation coefficient 115%), in accordance with their differing size. Oxygenation performance was greatest in the reed plants with larger above ground dry mass (>4 g dlsm), but no influence of the root dry mass on the OTC rate was found.