Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of non-ionic surfactants with different Hydrophilic-Lipophilic Balance (HLB) and their effect on flow properties in palm oil biodiesel

The use of surfactants has been known to reduce the interfacial tension between the two fluids. Surfactants have Hydrophilic-Lipophilic Balance (HLB) values that vary depending on their function. Previously, the addition of the Sorbitan Monooleate (SMO) had succeeded in reducing the Cloud Point (CP) by 4.2°C and the Cold Filter Plugging Point (CFPP) by 2°C. In this research, two different types of nonionic surfactants were tried, named the Sorbitan Monolaurate (SML) and the Sorbitan Trioleate (STO), which have HLB values of 8.6 and 1.8, respectively. This research aims to prove whether these surfactants can improve the flow properties of palm oil biodiesel by reducing the interfacial tension. Each surfactant was varied in palm oil biodiesel with a content of 1 and 2% at room temperature (±27°C) for a month with every week observation. Then, it was tested using the flow properties parameters, which are the CP, Pour Point (PP), and CFPP. The FTIR analysis is also carried out to determine the interactions between MG and these surfactants. The addition of 1% SML caused escalation in the CP, PP, and CFPP by 3.2, 3.5, and 6°C, respectively. Meanwhile, the addition of 2% STO caused a reduction in the CP and PP by 0.5 and 1°C, respectively but the CFPP tended to be constant.
Riboflavin Transporters and Breast Cancer Resistance Protein: Cimetidine-Riboflavin Interactions in the Mammary Gland

Mother's milk provides multiple benefits to the offspring. However, xenobiotics transferred into breast milk may pose a risk to the nursing infant. The breast cancer resistance protein (BCRP) actively transports xenobiotics into breast milk. BCRP also transports nutrients, like riboflavin, and together with recently identified riboflavin transporters (RFT), may provide a mechanism for riboflavin secretion into breast milk. Expression of RFT in the mammary gland remained unknown. Our objective was to characterize Bcrp and Rft mRNA expression in the mammary gland of FVB/N mice, and investigate a strategy to decrease excretion of BCRP-transported xenobiotics into the milk using riboflavin intervention. Rft and Bcrp mRNA were upregulated in the mammary gland of lactating mice. An intravenous riboflavin administration significantly reduced the levels of BCRP-transported cimetidine in milk. This study demonstrates the use of riboflavin to exploit the function of mammary BCRP in order to reduce xenobiotic secretion into breast milk.
Potential multidimensional behavioural impacts of differential infection in two fish populations

Parasites can influence the behaviour of their hosts, however investigations on how they may shape multiple personality traits are uncommon. The flatworm parasite Tylodelphys darbyi resides in the eyes of common bully, Gobiomorphus cotidianus, a locally common fish host to a range of other parasites that could also influence their behaviour. Here we assess how parasitism may be related to personality traits of two G. cotidianus populations; one where T. darbyi is highly abundant and one where the parasite is absent. We hypothesized that T. darbyi would have a stronger association with the different personality traits than other parasite taxa, and that the effects of infection on personality traits would vary between populations. Our results demonstrate that T. darbyi infections correlate with boldness, exploration, and activity within and among individuals. Further, we show that the relationship and therefore possible influence of other parasites, e.g., Apatemon sp. , on personality traits vary between two host populations. Our study has revealed potential patterns highlighting how parasitism may differentially contribute to behavioural and ecological divergence among host populations.
Decrease in the diversity of pyrrolizidine alkaloids of Senecio pterophorus after invasion

Pyrrolizidine alkaloids (PAs) are N-based plant secondary metabolites that function as chemical defenses against vertebrate and invertebrate herbivores. PAs can be highly variable at intraspecific level, both in their absolute and relative concentrations. Changes in the chemical composition of exotic plants when they invade a new environment have been poorly explored. Here we studied the biogeographical variation on PAs in Senecio pterophorus (Asteraceae) in the native region in Eastern South Africa, an expanded region in Western South Africa, and two introduced regions in Australia and Europe. PAs in S. pterophorus were represented by the highly toxic 1,2-unsaturated PAs and the less toxic 1,2-saturated PAs. Our results show a change in the plant chemical composition after invasion. Total PAs concentrations were highest in Australia compared to any other region. Plants from Europe contained the highest relative concentrations of 1,2-saturated PAs. The positive correlation between the chemical and the genetic distances estimated between populations suggests that the chemical profiles in the non-native regions were related to the plant dispersal routes. The decrease in the chemical diversity and the change in the absolute PAs concentrations in S. pterophorus after invasion may have consequences in the interactions between plants and herbivores in the novel habitats.
Comparison of three different surgical treatment methods, semi‐open closure, primary repair, and rotational flap for pilonidal sinus

Pilonidal sinus disease is a common infectious process that occurs on the natal cleft and sacrococcygeal part. The treatment of pilonidal sinus disease remains challenging and despite the existence of many non‐surgical and surgical methods, there is no consensus for the best treatment for this disease.
Psycho-behavioral momentum: Golf matchplay players’ perspectives

Abstract This study employed a qualitative in-match recording technique to understand how A-grade amateur golfers (n = 8) experience psycho-behavioral momentum during competitive matchplay. The aims were to capture, in real-time, thoughts and emotions associated with perceived changes to psycho-behavioral momentum, to understand what high-standard golfers perceive as significant triggers for both positive and negative psycho-behavioral momentum, and what strategies they might employ during a competition to maintain positive psycho-behavioral momentum and to overcome negative psycho-behavioral momentum. To complement the matchplay data, semi-structured group interviews were conducted to corroborate interpretive findings from the matchplay data and to discuss participants’ beliefs regarding momentum in the matchplay environment. Thematic analysis of both data sets revealed four themes to emerge from the participants’ statements pertaining to the genesis and maintenance of their experience of psycho-behavioral momentum: Unexpected Events (discrepancy between players’ expectations and reality, either positive or negative); Control (feeling in control: cognitions, emotions, behaviors, and outcomes; and appearing in control); Temporality (a perception that psycho-behavioral momentum cannot occur early in matches, sufficient match pressure and intensity is required to generate psycho-behavioral momentum); and Pressure (application and maintenance of psychological pressure upon opponent, and the management of pressure applied by opponent). Findings support existing conceptual models of momentum and extend the knowledgebase regarding how individuals experience momentum in competitive skill-based sports such as golf. Several strategies are suggested that could assist in building awareness of and managing psycho-behavioral momentum fluctuations. Lay summary: Information from competitive Australian matchplay golfers during interviews and matches revealed four key contributors to experiencing positive and negative psycho-behavioral momentum during rounds of matchplay golf. These key contributors are unexpected events, feeling and appearing in control, how far the game has progressed, and applying and coping with pressure. IMPLICATIONS FOR PRACTICE Psycho-behavioral momentum is an important aspect of applied sport psychology instruction and intervention. Sport psychology interventions aiming to enhance psycho-behavioral momentum, and thus performance, should support athletes to develop strategies that enhance feeling and appearing in control, along with skills to effectively manage psycho-behavioral momentum consequences of unexpected events. Sport psychology interventions should aim to build psycho-behavioral momentum early in competition, ideally building from training prior to events.
Carbon stocks and the use of shade trees in different coffee growing systems in the Peruvian Amazon

Abstract Agroforestry systems can play an important role in mitigating the effects of climate change given their capacity to increase tree diversity and to store more carbon than conventional farming. This study aims at assessing carbon stocks and the use of shade trees in different coffee growing systems in the Northeast Peruvian Amazon. Carbon stocks in trees were estimated by field-based measurements and allometric equations. Carbon stocks in dead wood, litter and soil (upper 60 cm) were determined using field sampling and laboratory analysis. The diversity analysis drew on the Shannon–Weiner diversity index, and focus groups were used to obtain information about the local use of shade trees. The total carbon stock in the polyculture-shaded coffee system was 189 t C/ha, while the Inga-shaded and unshaded systems totalled 146 and 113 t C/ha, respectively. The soil compartment contributed the largest carbon stock in the coffee growing systems and contained 67, 82 and 96% of the total carbon stock in the polyculture-shaded, Inga-shaded and unshaded coffee systems, respectively. The Shannon–Weiner index and tree species richness values were highest for the polyculture-shaded coffee system, with a total of 18 tree species identified as important sources of fodder, food, wood, firewood and medicine. Therefore, coffee agroforestry systems play a significant role in carbon storage, while promoting conservation of useful trees in agricultural landscapes in the Peruvian Amazon.
The impact of gamification on students learning engagement

Gamification is to use game elements in a non-game context to increase engagement between human and computer, on the other hand, to encourage in-demand for good behaviors in learning. This research tried to increase student engagements in learning by conducted Gamification technique especially in difficult subjects such as Programming Language courses. The previous work was shown that students dropped, failed, or withdrew from the course at rates of between 35% and 50%. Therefore the main objective of this study is to increase student engagements in learning programming subject, and also to measure the impact of game elements on student’s engagements. Finally, the findings have shown the score of game elements that have a good effect on student’s engagement in the experiment group.
Study of football social responsibility in Iran with Fuzzy cognitive mapping approach

Abstract Considering the increasing importance of social activities in sports, the aim of this study was to design a model of football social responsibility in Iran with a fuzzy cognitive mapping approach. For this purpose the data was collected by 13 in-depth interviews with experts in the field of football social studies in Iran selected through purposive and snowball sampling. By substantive and theoretical coding of interviews, the concepts of football social responsibility were identified and defined. The results showed football social responsibility in Iran includes 8 dimensions of moral, cultural, legal, socio-economic, transparency, benevolently, knowledge and socio-environmental responsibility. Examining the causal relationships between the blocks indicates that moral responsibility has the highest number of effective relationships. Also, the effect of moral responsibility on legal responsibility and transparency responsibility was more important than other relationships. Thus the concept of social responsibility of football can be considered from a broader perspective.
Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model

Multiple longitudinal outcomes are theoretically easily modelled via extension of the generalized linear mixed effects model. However, due to computational limitations in high dimensions, in practice these models are applied only in situations with relatively few outcomes. We adapt the solution proposed by Fieuws and Verbeke (2006) to the Bayesian setting: fitting all pairwise bivariate models instead of a single multivariate model, and combining the Markov Chain Monte Carlo (MCMC) realizations obtained for each pairwise bivariate model for the relevant parameters. We explore importance sampling as a method to more closely approximate the correct multivariate posterior distribution. Simulation studies show satisfactory results in terms of bias, RMSE and coverage of the 95% credible intervals for multiple longitudinal outcomes, even in scenarios with more limited information and non-continuous outcomes, although the use of importance sampling is not successful. We further examine the incorporation of a time-to-event outcome, proposing the use of Bayesian pairwise estimation of a multivariate GLMM in an adaptation of the corrected two-stage estimation procedure for the joint model for multiple longitudinal outcomes and a time-to-event outcome (Mauff et al., 2020, Statistics and Computing). The method does not work as well in the case of the corrected two-stage joint model; however, the results are promising and should be explored further.
Teaching Applications and Implications of Blockchain via Project-Based Learning: A Case Study.

This paper presents student projects analyzing or using blockchain technologies, created by students enrolled in courses dedicated to teaching blockchain, at two different universities during the 20182019 academic year. Students explored perceptions related to storing private healthcare information on a blockchain, managing the security of Internet of Things devices, maintaining public governmental records, and creating smart contracts. The course designs, which were centered around project-based learning, include self-regulated learning and peer feedback as ways to improve student learning. Students either wrote a research paper or worked in teams on a programming project to build and deploy a blockchain-based application using Solidity, a programming language for writing smart contracts on various blockchain platforms. For select student papers, this case study describes research methods and outcomes and how students worked together or made use of peer feedback to improve upon drafts of research questions and abstracts. For a development project in Solidity, this study presents the issues at hand along with interview results that guided the implementation. Teams shared lessons learned with other teams through a weekly status report to the whole class. While available support for the Solidity teams was not ideal, students learned to use available online resources for creating and testing smart contracts. Our findings suggest that a project-based learning approach is an effective way for students to expand and develop their knowledge of emerging technologies, like blockchain, and apply it in a variety of industries.
Empirical Analysis of Decision Making of an AI Agent on IBM’s 5Q Quantum Computer

A recent work has shown that using an ion trap quantum processor can speed up the decision making of a reinforcement learning agent. Its quantum advantage is observed when the external environment changes, and then agent needs to relearn again. One character of this quantum hardware system discovered in this study is that it tends to overestimate the values used to determine the actions the agent will take. IBM’s five qubit superconducting quantum processor is a popular quantum platform. The aims of our study are twofold. First we want to identify the hardware characteristic features of IBM’s 5Q quantum computer when running this learning agent, compared with the ion trap processor. Second, through careful analysis, we observe that the quantum circuit employed in the ion trap processor for this agent could be simplified. Furthermore, when tested on IBM’s 5Q quantum processor, our simplified circuit demonstrates its enhanced performance over the original circuit on one of the hard learning tasks investigated in the previous work. We also use IBM’s quantum simulator when a good baseline is needed to compare the performances. As more and more quantum hardware devices are moving out of the laboratory and becoming generally available to public use, our work emphasizes the fact that the features and constraints of the quantum hardware could take a toll on the performance of quantum algorithms.
Design and Analysis of a Water Pipe Leakage Sensor

Water pipeline leakage is a common problem in almost every country in the world which has become a shared concern today. While some regions do not even have the access to clean and treated water, others have seen millions of litres of water wasted every day due to leakages which probably could have been sufficient to serve the needy. In most cases, the detrimental effects associated with the occurrence of leaks may present serious problems and therefore, leaks must be quickly detected, located and repaired. Recent advances in sensor technology have resulted in a wide application of sensor networks for the purpose of leakage management. Currently, researchers have gone as far as putting the sensors inside the pipeline itself to identify, locate and estimate the leak size. In the current study, CFD simulations were used to find the drag coefficient associated with the designs prepared which is an important parameter in this study. The work has been validated with the previous work. The main outcome from the study was the drag coefficient produced from the proposed model is significantly higher than the reference design. For the water pipe case, this a favourable outcome.
Optimization approaches of industrial serial manipulators to improve energy efficiency: A review

The objective of this paper is to provide a comprehensive review of existing approaches and techniques developed in the field of industrial robotics to make it energy efficient. The usage of industrial manipulators is increasing globally due to its repeatability and superiority. The considerable energy demand is also predicted because of the increased application of robots in industries, this can be overcome by making energy-efficient manipulators. Different approaches are already established in this field to make robots energy efficient such as; elimination of needless densities by topology optimization, selection of optimal path using trajectory optimization, usage of light-weight components, analysing speed, and providing energy storage devices, etc. Among different available approaches, major three methodologies are reviewed in this paper. The first group comprises the topology optimization method that optimizes the design space by eliminating needless densities based on given boundary conditions and constraints. Further, the topology optimization method sub-classified into two types based on loading conditions such as static analysis and dynamic analysis. The second group comprises trajectory optimization of robots; this is achieved by selecting the optimal path of the work-cycle, or minimization of torque. Again trajectory optimization classified into point-to-point and multi-point optimization. The third group includes the application of light-weight structural components in the field of industrial robotics and its challenges. Presented work will be useful to analyze different approaches to make the robot as energy efficient.
Individual vigilance profiles in flocks of House Sparrows (Passer domesticus)

Individual vigilance against threats typically decreases with group size. However, group size often explains a small amount of variation in vigilance, suggesting that other factors such as individual differences might contribute. For instance, individuals could maintain different vigilance levels overall and also respond differently to variation in group size. We investigated individual variation in vigilance and its patterns of plasticity in flocks of House Sparrows (Passer domesticus (Linnaeus, 1758)). We carried out observations at one provisioned site and used multiple observations of the same individuals (n = 14) in flocks of different sizes over two consecutive months. The typical decline in vigilance with flock size occurred at the population level. Controlling for food density, flock size, time of year, and sex, we documented consistent individual differences in various measurements of vigilance. Plasticity of vigilance adjustments to variation in flock size occurred for the frequency of high vigilance postures. Male House Sparrows with larger bibs, which signal higher dominance status, tended to spend less time vigilant and obtained food at a higher rate, supporting a state-dependent explanation for the origin of individual vigilance profiles. Individual differences can contribute to explaining the large scatter in the relationship between vigilance and group size in many species.
Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities

Flow cytometry is able measure up to 50.000 cells in various dimensions in seconds of time. This large amount of data gives rise to the possibility of making predictions at the single-cell level, however, applied to bacterial populations a systemic investigation lacks. In order to combat this deficiency, we cultivated twenty individual bacterial populations and measured them through flow cytometry. By creating in silico communities we are able to use supervised machine learning techniques in order to examine to what extent single-cell predictions can be made; this can be used to identify the community composition. We show that for more than half of the communities consisting out of two bacterial populations we can identify single cells with an accuracy >90%. Furthermore we prove that in silico communities can be used to identify their in vitro counterpart communities. This result leads to the conclusion that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of bacterial flow cytometric data and for the experimental study of low-complexity communities.
Efficient sparse collective communication and its application to accelerate distributed deep learning

Efficient collective communication is crucial to parallel-computing applications such as distributed training of large-scale recommendation systems and natural language processing models. Existing collective communication libraries focus on optimizing operations for dense inputs, resulting in transmissions of many zeros when inputs are sparse. This counters current trends that see increasing data sparsity in large models. We propose OmniReduce, an efficient streaming aggregation system that exploits sparsity to maximize effective bandwidth use by sending only non-zero data blocks. We demonstrate that this idea is beneficial and accelerates distributed training by up to 8.2x. Even at 100 Gbps, OmniReduce delivers 1.4--2.9x better performance for network-bottlenecked DNNs.
Characterization of a set of common wheat–Hordeum chilense chromosome 7Hch introgression lines and its potential use in research on grain quality traits

M.G. Mattera received a fellowship from the Ministerio de Economia, Industria y Competitividad (BES-2012-055961). This research was funded by grant AGL2014-53195R from the Ministerio de Economia, Industria y Competitividad, Spain (MINECO), with cofinancing from the European Regional Development Fund.