Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Diversity and Occurrence of Filamentous Fungi in Smallholder Dairy Cattle Feeds and Feedstuffs in South Africa

This study investigated 65 (35 in summer and 30 in winter) smallholder dairy cattle feeds from Free State and Limpopo provinces in South Africa from 2018 to 2019 for fungal contamination and assessed the impacts of seasonal variation on fungal contamination levels, isolation frequency, and diversity. Samples were examined for fungal contamination using macro- and microscopic approaches, and their identities were confirmed by molecular means. A total of 217 fungal isolates from 14 genera, including Aspergillus, Fusarium, and Penicillium, were recovered from feeds from both seasons. The most prevalent fungal species recovered were A. fumigatus and P. crustosum. Mycological analyses showed that 97% of samples were contaminated with one or more fungal isolates, with the summer fungal mean level (6.1 × 103 to 3.0 × 106 CFU/g) higher than that of feeds sampled during winter (mean level: 1.1 × 103 to 4.1 × 105 CFU/g). Independent sample t-test revealed that the isolation frequencies of the genera Aspergillus and Fusarium were significantly (p ≤ 0.05) higher in summer than winter, while Penicillium prevalence in both seasons was not statistically (p > 0.05) different. Furthermore, the Shannon–Weiner diversity index (H′) revealed a higher fungal diversity in summer (H′ = 2.8) than in winter (H′ = 2.1). This study on fungal contamination could be used for future fungal control and mycotoxin risk management in South Africa.
Equivariant Bifurcation and Absolute Irreducibility in $\R^8$ A contribution to the Ize conjecture

M. Field [5] refers to an unpublished work by J. Ize for a result that loss of stability through an absolutely irreducible representation of a compact Lie group leads to bifurcation of steady states. The main ingredient of the proof is the hypotheses, that for an absolutely irreducible representation of a compact Lie group there exists a closed subgroup whose fixed point space is odd dimensional. Then, using Brouwer degree, one gets the result. We refer to the hypotheses that for an absolutely irreducible representation of a compact Lie group there exists at least one subgroup with an odd dimensional fixed point space as the algebraic Ize conjecture (AIC). Lauterbach and Matthews [10] have shown that the (AIC) is in general not true. In fact they have constructed three infinite families of finite subgroups of SO{4} which act absolutely irreducibly on $\R^4$ and for each of them any isotropy subgroup has an even dimensional fixed point space. Moreover in [10] it is shown that in spite of this failure of the (AIC) the original conjecture is true at least for groups in two of these three families. In this paper we show a similar bifurcation result for the third family defined in [10]. We go on and construct a family of groups acting absolutely irreducibly on $\R^8$ which have only even dimensional fixed point spaces. Then we discuss the steady state bifurcations in this case. We end this paper with a discussion on how to extend the results in [10] to larger sets of groups which act on $\R^{4}$.
Your Language”: A Literature Review on the Use of Dirty words in Improving Memory for Adult Learners

paper look effects as students. The major lobes that taught are the frontal, the temporal, the and the occipital lobes. These provide executive function, smell ABSTRACT Treatments for neurological disorders have employed the use of dirty language as a method of overcoming language deficits, especially for those who have experienced strokes. Because of the linkage of the cursing language pathway and the memory, there has been some discussion on how the use of dirty language could improve recall in neurologically intact individuals. Twenty-six [1-26] studies were included within this literature review. Inclusion criteria included use of swears words as defined by Jay [10] and attention to memory. This literature review looks at the neurobiological basis of the limbic pathway, explores previous research, and proposes a study to see the effects of the use of dirty language within the adult learner space to increase the overall retention of information for the students.
Market response and future performance of inefficient investment: Over-investment or under-investment

There have been many studies on the market response to investment spending, but only a few have examined the market response to the issue of over-investment or under-investment. This study examines the effect of the issue on market response and future financial performance. The sample includes large-cap companies listed on the Indonesia Stock Exchange (IDX) for 2016–2021. Samples must have at least 120 active trading days for each year. Two hundred and thirty-two observations meet the qualifications. This study adopts the investment inefficiency model developed by previous studies to measure over-investment or under-investment. Residual inefficient investment models are used as over-investment or under-investment scores, in addition to the dummy of the residual category. Market response is measured by cumulative abnormal returns (CAR), market capitalization (MCAP), and market-to-book value (MTB). Meanwhile, a firm’s performance uses return on assets (ROA) and return on equity (ROE). The results show that the coefficient of the inefficient investment variable, using both the residual value and the dummy variable, shows a negative direction, which means the market responds negatively to over-investment or under-investment. However, the value of t is significant at the &lt;0.01 level on the market response variable as measured by MTB, but not significant for the other two proxies. Thus, hypothesis 1 is supported, although not for all market response proxies. The value of the inefficient investment coefficient also shows a negative direction when testing hypothesis 2 and is significant at the &lt;0.1 level. These results are consistent with future performance variables measured by ROA and ROE. AcknowledgmentThe study was supported by PDUPT (Higher Education Primary Research Grant) from the Ministry of Education, Culture, Research and Technology, Government of Indonesia.
A Review of In Situ Methods—Solid Phase Adsorption Toxin Tracking (SPATT) and Polar Organic Chemical Integrative Sampler (POCIS) for the Collection and Concentration of Marine Biotoxins and Pharmaceuticals in Environmental Waters

Solid Phase Adsorption Toxin Tracking (SPATT) and Polar Organic Chemical Integrative Sampler (POCIS) are in situ methods that have been applied to pre-concentrate a range of marine toxins, pesticides and pharmaceutical compounds that occur at low levels in marine and environmental waters. Recent research has identified the widespread distribution of biotoxins and pharmaceuticals in environmental waters (marine, brackish and freshwater) highlighting the need for the development of effective techniques to generate accurate quantitative water system profiles. In this manuscript, we reviewed in situ methods known as Solid Phase Adsorption Toxin Tracking (SPATT) and Polar Organic Chemical Integrative Sampler (POCIS) for the collection and concentration of marine biotoxins, freshwater cyanotoxins and pharmaceuticals in environmental waters since the 1980s to present. Twelve different adsorption substrates in SPATT and 18 different sorbents in POCIS were reviewed for their ability to absorb a range of lipophilic and hydrophilic marine biotoxins, pharmaceuticals, pesticides, antibiotics and microcystins in marine water, freshwater and wastewater. This review suggests the gaps in reported studies, outlines future research possibilities and guides researchers who wish to work on water contaminates using Solid Phase Adsorption Toxin Tracking (SPATT) and Polar Organic Chemical Integrative Sampler (POCIS) technologies.
Collapse of nonlinear gravitational waves in moving-puncture coordinates

We study numerical evolutions of nonlinear gravitational waves in moving-puncture coordinates. We adopt two different types of initial data\char22{}Brill and Teukolsky waves\char22{}and evolve them with two independent codes producing consistent results. We find that Brill data fail to produce long-term evolutions for common choices of coordinates and parameters, unless the initial amplitude is small, while Teukolsky wave initial data lead to stable evolutions, at least for amplitudes sufficiently far from criticality. The critical amplitude separates initial data whose evolutions leave behind flat space from those that lead to a black hole. For the latter we follow the interaction of the wave, the formation of a horizon, and the settling down into a time-independent trumpet geometry. We explore the differences between Brill and Teukolsky data and show that for less common choices of the parameters\char22{}in particular negative amplitudes\char22{}Brill data can be evolved with moving-puncture coordinates and behave similarly to Teukolsky waves.
Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: 3-Fold Analysis

Background: Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. Objective: The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. Methods: Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. Results: The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both preand postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). JMIR Mhealth Uhealth 2019 | vol. 7 | iss. 7 | e13209 | p.1 http://mhealth.jmir. org/2019/7/e13209/ (page number not for citation purposes) Doryab et al JMIR MHEALTH AND UHEALTH
ScholarWorks @ Georgia State University ScholarWorks @ Georgia State University Introduction to “Justice in Animals” Introduction to “Justice in Animals”

In the last decade, there has been an explosion of work investigating non-human species’ behavior as it relates to the human sense of fairness and justice. This work has provided a much-needed evolutionary perspective on humans, and highlighted ways in which humans’ behavior is both similar to and different from that of other species. In this concluding paper, we outline the major threads of the work highlighted in this and the previous special issues of Social Justice Research and provide thoughts on future directions for the field. This is a very exciting time in our exploration of the evolution of human justice and fairness, and we eagerly await the developments of the next decade.
A Pedagogy of Constraints: How Self-Imposed Limitations Influence Art-Making and Teaching

A Pedagogy of Constraints: How Self-Imposed Limitations Influence Art-Making and Teaching Emmalee Glauser Powell Department of Art, BYU Master of Arts This study explores how self-imposed limitations affect anxieties about art-making and the art-making process. As a teacher, I was interested in how limitations affected student artmaking. I used arts-based research methodology to explore spiritual and personal quandaries in my own life through the process of art-making. A consistent thread throughout this investigation was using the process of making art as a way to gain understanding about my own life and teaching. I was also able to create a culture of vulnerability and honesty in my classroom and help my students embrace themselves and their physical, emotional, and situational limitations through the art-making process.
Core collapse and horizontal-branch morphology in Galactic globular clusters

Context. Stellar collision rates in globular clusters (GCs) do not appear to correlate with horizontal branch (HB) morphology, sug- gesting that dynamics does not play a role in the second-parameter problem. However, core densities and collision rates derived from surface-brightness may be significantly underestimated as the surface-brightness profile of GCs is not necessarily a good indicator of the dynamical state of GC cores. Core-collapse may go unnoticed if high central densities of dark remnants are present. Aims. We test whether GC HB morphology data supports a dynamical contribution to the so-called second-parameter effect. Methods. To remove first-parameter dependence we fitted the maximum effective temperature along the HB as a function of metal- licity in a sample of 54 Milky Way GCs. We plotted the residuals to the fit as a function of second-parameter candidates, namely dynamical age and total luminosity. We considered dynamical age (i.e. the ratio between age and half-light relaxation time) among possible second-parameters. We used a set of direct N-body simulations, including ones with dark remnants to illustrate how core density peaks, due to core collapse, in a dynamical-age range similar to that in which blue HBs are overabundant with respect to the metallicity expectation, especially for low-concentration initial conditions. Results. GC total luminosity shows nonlinear behavior compatible with the self-enrichment picture. However, the data are amenable to a different interpretation based on a dynamical origin of the second-parameter effect. Enhanced mass-stripping in the late red-giant- branch phase due to stellar interactions in collapsing cores is a viable candidate mechanism. In this picture, GCs with HBs bluer than expected based on metallicity are those undergoing core-collapse.
Formation of a protocluster: A virialized structure from gravoturbulent collapse. I. Simulation of cluster formation in a collapsing molecular cloud

Context. Stars are often observed to form in clusters and it is therefore important to understand how such a region of concentrated mass is assembled out of the diffuse medium. The properties of such a region eventually prescribe the important physical mechanisms and determine the characteristics of the stellar cluster. Aims. We study the formation of a gaseous protocluster inside a molecular cloud and associate its internal properties with those of the parent cloud by varying the level of the initial turbulence of the cloud with a view to better characterize the subsequent stellar cluster formation. Methods. We performed high resolution magnetohydrodynamic (MHD) simulations of gaseous protoclusters forming in molecular clouds collapsing under self-gravity. We determined ellipsoidal cluster regions via gas kinematics and sink particle distribution, permitting us to determine the mass, size, and aspect ratio of the cluster. We studied the cluster properties, such as kinetic and gravitational energy, and made links to the parent cloud. Results. The gaseous protocluster is formed out of global collapse of a molecular cloud and has non-negligible rotation owing to angular momentum conservation during the collapse of the object. Most of the star formation occurs in this region, which occupies only a small volume fraction of the whole cloud. This dense entity is a result of the interplay between turbulence and gravity. We identify such regions in simulations and compare the gas and sink particles to observed star-forming clumps and embedded clusters, respectively. The gaseous protocluster inferred from simulation results presents a mass-size relation that is compatible with observations. We stress that the stellar cluster radius, although clearly correlated with the gas cluster radius, depends sensitively on its definition. Energy analysis is performed to confirm that the gaseous protocluster is a product of gravoturbulent reprocessing and that the support of turbulent and rotational energy against self-gravity yields a state of global virial equilibrium, although collapse is occurring at a smaller scale and the cluster is actively forming stars. This object then serves as the antecedent of the stellar cluster, to which the energy properties are passed on. Conclusions. The gaseous protocluster properties are determined by the parent cloud out of which it forms, while the gas is indeed reprocessed and constitutes a star-forming environment that is different from that of the parent cloud.
A NEW METHOD FOR CLASSIFYING FLARES OF UV Ceti TYPE STARS: DIFFERENCES BETWEEN SLOW AND FAST FLARES

In this study, a new method is presented to classify flares derived from the photoelectric photometry of UV Ceti type stars. This method is based on statistical analyses using an independent samples t-test. The data used in analyses were obtained from four flare stars observed between 2004 and 2007. The total number of flares obtained in the observations of AD Leo, EV Lac, EQ Peg, and V1054 Oph is 321 in the standard Johnson U band. As a result flares can be separated into two types, slow and fast, depending on the ratio of flare decay time to flare rise time. The ratio is below 3.5 for all slow flares, while it is above 3.5 for all fast flares. Also, according to the independent samples t-test, there is a difference of about 157 s between equivalent durations of slow and fast flares. In addition, there are significant differences between amplitudes and rise times of slow and fast flares.
Spontaneous ring formation in wind-emitting accretion discs

Rings and gaps have been observed in a wide range of proto-planetary discs, from young systems like HLTau to older discs like TW Hydra. Recent disc simulations have shown that magnetohydrodynamic (MHD) turbulence (in both the ideal or non-ideal regime) can lead to the formation of rings and be an alternative to the embedded planets scenario. In this paper, we have investigated the way in which these ring form in this context and seek a generic formation process, taking into account the various dissipative regimes and magnetisations probed by the past simulations. We identify the existence of a linear and secular instability, driven by MHD winds, and giving birth to rings of gas that have a width larger than the disc scale height. We show that the linear theory is able to make reliable predictions regarding the growth rates, the contrast and spacing between ring and gap, by comparing these predictions to a series of 2D (axisymmetric) and 3D MHD numerical simulations. In addition, we demonstrate that these rings can act as dust traps provided that the disc is sufficiently magnetised, with plasma beta lower than 104. Given its robustness, the process identified in this paper could have important implications, not only for proto-planetary discs but also for a wide range of accreting systems threaded by large-scale magnetic fields.
The Effectiveness Of Mindfulness On Blood Pressure And Brain Wave Of Elderly People

Blood pressure in the elderly is considered as one of the health disturbing factors. In this study, we seek to evaluate the effectiveness of mindfulness-based therapy on blood pressure and brain waves in the elderly. Method The present study was a quasi-experimental with experimental and control groups. In this study, 8 elderly people were trained in 8 mindfulness techniques one by one. The brain waves and blood pressure of the subjects were then compared with those in the control group. In this study, a sphygmomanometer and electroencephalography device were used. The results showed that the mean blood pressure of the experimental group decreased compared to the control group in the post-test stage. Also, the alpha and theta brain waves of the experimental group increased compared to the control group. According to the findings of the present study, mindfulness method can reduce blood pressure in elderly people with high blood pressure and on the other hand, increase brain waves associated with relaxation, namely alpha and theta.
Matrix and tensor decompositions for training binary neural networks

This paper is on improving the training of binary neural networks in which both activations and weights are binary. While prior methods for neural network binarization binarize each filter independently, we propose to instead parametrize the weight tensor of each layer using matrix or tensor decomposition. The binarization process is then performed using this latent parametrization, via a quantization function (e.g. sign function) applied to the reconstructed weights. A key feature of our method is that while the reconstruction is binarized, the computation in the latent factorized space is done in the real domain. This has several advantages: (i) the latent factorization enforces a coupling of the filters before binarization, which significantly improves the accuracy of the trained models. (ii) while at training time, the binary weights of each convolutional layer are parametrized using real-valued matrix or tensor decomposition, during inference we simply use the reconstructed (binary) weights. As a result, our method does not sacrifice any advantage of binary networks in terms of model compression and speeding-up inference. As a further contribution, instead of computing the binary weight scaling factors analytically, as in prior work, we propose to learn them discriminatively via back-propagation. Finally, we show that our approach significantly outperforms existing methods when tested on the challenging tasks of (a) human pose estimation (more than 4% improvements) and (b) ImageNet classification (up to 5% performance gains).