Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of algae derived pure β–Glucan on In vitro rumen fermentation

The major purpose of this study was to determine how varying doses of algae-derived pure β–glucan affected in vitro gas generation, volatile fatty acid (VFA) concentrations, methane production, and protozoa populations. Different doses of β–glucan [i.e., 0, 50, 100, 150, and 200 mg/kg feed (DM basis)] were applied to corn silage as experimental treatments. After 6–96 hours of incubation, the dose of 200 mg/kg of DM β–glucan reduced total gas production compared to control (P<0.01). The concentration of total VFA decreased quadratically (P<0.01) as the amount of β–glucan inclusion decreased (except for 200 mg/kg DM) when compared to the control group. The total VFA concentration was found to be the lowest (P<0.01) at 50, 100, and 150 mg/kg DM β–glucan than the other doses. Propionate and butyrate concentrations increased linearly (P<0.01) in the β–glucan supplemented groups, except for the 50 mg/kg DM dosage. When compared to the control group, all doses of β–glucans lowered acetate and the acetate: propionate ratio linearly and quadratically (P<0.01). The addition of β–glucans reduced the number of protozoa linearly (except at the lowest dose) and reduced the methane generation linearly and quadratically (P<0.01). The concentration of NH 3 -N did not differ (Linear, P=0.12; Quadratic, P=0.19) between treatments. The key findings were that β–glucan acted as a rumen modulator, and levels of more than 50 mg/kg of feed DM functioned as a potential methane regulator in the rumen due to reduced acetate and acetate to propionate ratio.
Development and validation of a behavioral acclimation protocol for cats to respiration chambers used for indirect calorimetry studies

Cats exposed to stressful and novel environments initiate stress responses as evidenced by behavioural and physiological changes which, in turn, modify energy requirement and metabolism leading to the collection of unreliable results from nutritional trials conducted on stressed cats. 14 cats (10 ± 2 months) were subjected to an 11 week acclimation procedure to adapt cats to restriction within a chamber used for calorimetry studies. Cats were acclimated to the chambers on weeks 1–3, the study room on weeks 4–6 and increasing restriction periods of 1 to 20 hrs within the chamber on weeks 7–11. Ten additional cats (11 ± 1 month), used as controls, were subjected to 5 hrs of restriction without prior chamber or study room exposure to compare responses. Stress level, feed intake, fearfulness and eliminations were recorded. Cat‐Stress‐Scores (CSS) and latencies to approach a novel object peaked on weeks 4 and 7 (P<0.05). CSS declined with exposure to a novel experience and on week 11, stress levels were low and consistent (P<0.05). CSS was greater in un‐acclimated as compared to acclimated cats (P<0.05). In conclusion, acclimation protocols prepare cats for repeated, temporary restriction, whereas short acclimations do not. A step up acclimation procedure with behavioral indices of stress should be utilized to prepare cats for research that necessitates restriction. Supported by: The Procter and Gamble Co., Pet Care, Ohio, USA.
Randomized Pivoting and Spectrum-Revealing Bounds in Numerical Linear Algebra

In the first part of this dissertation, we explore a novel randomized pivoting strategy to efficiently improve the reliability and quality of the LU factorization. Gaussian elimination with partial pivoting (GEPP) has long been among the most widely used methods for computing the LU factorization of a given matrix. However, this method is also known to fail for matrices that induce large element growth during the factorization process. We propose a new scheme, Gaussian elimination with randomized complete pivoting (GERCP) for the efficient and reliable LU factorization of a given matrix. GERCP satisfies GECP (Gaussian elimination with complete pivoting) style element growth bounds with high probability, yet costs only marginally more than GEPP in terms of algorithmic complexity and run-time. Our numerical experimental results strongly suggest that GERCP is as reliable as GECP and as efficient as GEPP for computing the LU factorization. In the second part, this dissertation provides tighter and simplified analyses of various popular low-rank matrix approximation algorithms included randomized subspace iteration and column/row selection based methods. We derive new bounds and unify them with other existing bounds under the title Spectrum-Revealing Bounds. These bounds demonstrate how certain structure in the decay of the spectrum of a matrix help to ``reveal'' an increasingly accurate estimate to the low-rank matrix approximation. We provide real world applications that demonstrate the qualitative value of our bounds for anyone using low-rank matrix approximations. In the case of randomized subspace iteration, we also dramatically improve and simplify the probabilistic analysis from previous works using intuitive and concise techniques. Lastly, we apply the idea of efficient low-rank matrix approximation in the presence of spectral decay to help speed up sparse principle components analysis (SPCA). We also develop novel lower bounds on the variance captured by each sparse principle component obtained after deflation.
SOUND EVENT LOCALIZATION AND DETECTION BASED ON CRNN USING ADAPTIVE HYBRID CONVOLUTION AND MULTI-SCALE FEATURE EXTRACTOR Technical

In this report, we present our method for Detection and Classiﬁ-cation of Acoustic Scenes and Events (DCASE) 2021 challenge task 3: Sound Event Localization and Detection with Directional Interference (SELDDI). In this paper, we propose a method based on Adaptive Hybrid Convolution (AHConv) and multi-scale feature extractor. The square convolution shares the weight in each T-F bin of the ﬁxed area in feature map, that is limited. In order to address this problem, we propose a AHConv mechanism instead of square convolution to obtain time and frequency dependencies. We also explored multi-scale feature extractor which can integrate information from very local to exponentially large receptive ﬁeld within the block. In order to adaptive recalibrate the feature maps after convolutional operation, we designed an adaptive attention block which are largely embodied in the AHConv. On TAU-NIGENS Spatial Sound Events 2021 development dataset, our systems demonstrate a signiﬁcant improvement over the baseline system. Only the ﬁrst-order Ambisonics (FOA) dataset was considered in this experiment.
Microwaves as a probe of quantum dot circuits: from Kondo dynamics to mesoscopic quantum electrodynamics

This thesis uses microwaves as probe of carbon nanotube quantum dot circuits. In a first experiment, a microwave excitation is directly applied to a circuit electrode for a quantum dot in the Kondo regime. We provide the first frequency-amplitude characterisation of the Kondo zero-bias conductance. Preliminary data are consistent with predicted universal behaviour. We present two other experiments, where quantum dot circuits are embedded in microwave resonators. Cavity photons probe charge relaxation resistance and photon-emission in a quantum dot coupled to normal and superconducting reservoirs in presence of Coulomb repulsion. Our observations validate a modelling in terms of the circuit linear response. We also present the first implementation of a Cooper pair splitter in cavity. The strong coupling regime is achieved, a premiere with quantum dot circuits. Our findings support the idea, that mesoscopic quantum electrodynamics is a fruitful toolbox in the context of both fields of quantum transport and quantum information science.
Decision Criterion Dynamics in Animals Performing an Auditory Detection Task

Classical signal detection theory attributes bias in perceptual decisions to a threshold criterion, against which sensory excitation is compared. The optimal criterion setting depends on the signal level, which may vary over time, and about which the subject is naïve. Consequently, the subject must optimise its threshold by responding appropriately to feedback. Here a series of experiments was conducted, and a computational model applied, to determine how the decision bias of the ferret in an auditory signal detection task tracks changes in the stimulus level. The time scales of criterion dynamics were investigated by means of a yes-no signal-in-noise detection task, in which trials were grouped into blocks that alternately contained easy- and hard-to-detect signals. The responses of the ferrets implied both long- and short-term criterion dynamics. The animals exhibited a bias in favour of responding “yes” during blocks of harder trials, and vice versa. Moreover, the outcome of each single trial had a strong influence on the decision at the next trial. We demonstrate that the single-trial and block-level changes in bias are a manifestation of the same criterion update policy by fitting a model, in which the criterion is shifted by fixed amounts according to the outcome of the previous trial and decays strongly towards a resting value. The apparent block-level stabilisation of bias arises as the probabilities of outcomes and shifts on single trials mutually interact to establish equilibrium. To gain an intuition into how stable criterion distributions arise from specific parameter sets we develop a Markov model which accounts for the dynamic effects of criterion shifts. Our approach provides a framework for investigating the dynamics of decisions at different timescales in other species (e.g., humans) and in other psychological domains (e.g., vision, memory).
Cognitive Rehabilitation: Mild Traumatic Brain Injury and Relevance of OTPF

There is increased awareness of the long-term cognitive sequelae of mild traumatic brain injury (mTBI). Therefore, researchers and clinicians have developed and tested cognitive training protocols to address these challenges. The current review summarized literature that examined existing cognitive rehabilitation/training programs. Specifically, the review listed the impact of these programs on functional domains informed by the Occupational Therapy Practice Framework (OTPF). Literature between the years 2008 and 2022 was gathered from nine databases. Results indicate that several cognitive rehabilitation programs have proven to positively influence domains of occupation, client factors, performance, and context. Occupational therapy practitioners have an opportunity to engage in mTBI management. Furthermore, adopting domains of OTPF may guide assessments, treatment planning, and long-term follow-up.
Scalar quantum electrodynamics in de Sitter space from the non-perturbative renormalization group

We apply the non-perturbative renormalization group techniques to integrate out strongly fluctuating infrared modes for a scalar field in de Sitter universe. Using an spontaneously broken potential that is already know, using the two non-perturbative techniques, the stochastic formalism and the renormalization group approach, to spontaneously recover the symmetry due to the strongly fluctuating IR modes. And adding photons interaction to the effective potential we show that the symmetry restoration gets enhanced by the photons. Finally we compare the know results obtained using both methods for the scalar mass and the photon mass, that is know to acquire a non-vanishing mass during inflation, allowing us to compare both methods. Thesis Supervisor: Dr. Tomislav Prokopec Title: Associate Professor
An EM Approach to Non-autoregressive Conditional Sequence Generation

A non-autoregressive Transformer (Vaswani et al., 2017) a Transformer encoder and a non-autoregressive Transformer decoder. More concretely, both encoder and decoder consist of two types of layers: the multi-head attention layer Attn and the token-wise feed-forward layer FF, with both layers having a skip connection1 (He et al., 2016). The encoder block in the non-autoregressive Transformer tenc maps an input X ∈ Rd×n consisting of d-dimensional embeddings of n tokens to an output tenc(X) ∈ Rd×m. It consists of a self-attention layer and a feed-forward layer. The decoder block in the non-autoregressive Transformer tdec maps an input Y ∈ Rd×m consisting of d-dimensional embeddings of m tokens and a context X ∈ Rd×n consisting of d-dimensional embeddings of n tokens to an output tdec(X,Y ) ∈ Rd×m. It consists of a self-attention layer, a encoder-decoder attention layer, and a feed-forward layer:
Practical Assessment, Research, and Evaluation Practical Assessment

Today, researchers have the ability to conduct their investigations in a number of different manners, including both traditional testing using university subject pool participants and the more recent method of online recruitment. Although the use of internet participants is becoming more popular, this area of research is still very much in its infancy and needs further examination. Additionally, alternative web-based platforms need to be investigated because much of the literature has focused on using Amazon.com’s Mechanical Turk (MTurk). Therefore, the current study recruited an internet population using the website Reddit, and compared them to a traditional undergraduate sample to learn more about this web-based platform. The results demonstrated similarities and distinctions between the two samples. Furthermore, previous findings in the psychological well-being literature were replicated. As a whole, the participants recruited from Reddit provided high-quality data that were inexpensive and comparable to the responses gathered using undergraduate participants. We conclude that this website appears to be a promising tool for the field of psychological assessment, research, and evaluation.
Fast Locality-Sensitive Hashing for Approximate Near Neighbor Search

The Indyk-Motwani Locality-Sensitive Hashing (LSH) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution $\mathcal{H}$ over locality-sensitive hash functions that partition space. For a collection of $n$ points, after preprocessing, the query time is dominated by $O(n^{\rho} \log n)$ evaluations of hash functions from $\mathcal{H}$ and $O(n^{\rho})$ hash table lookups and distance computations where $\rho \in (0,1)$ is determined by the locality-sensitivity properties of $\mathcal{H}$. It follows from a recent result by Dahlgaard et al. (FOCS 2017) that the number of locality-sensitive hash functions can be reduced to $O(\log^2 n)$, leaving the query time to be dominated by $O(n^{\rho})$ distance computations and $O(n^{\rho} \log n)$ additional word-RAM operations. We state this result as a general framework and provide a simpler analysis showing that the number of lookups and distance computations closely match the Indyk-Motwani framework, making it a viable replacement in practice. Using ideas from another locality-sensitive hashing framework by Andoni and Indyk (SODA 2006) we are able to reduce the number of additional word-RAM operations to $O(n^\rho)$.
On Modified Log Burr XII Distribution

In this paper, we present a modified Log Burr XII (MLBXII) distribution developed on the basis of a generalized log Pearson differential equation. This distribution is also obtained from a compounding mixture of distributions. Moments, inequality measures, uncertainty measures and reliability measures are theoretically established. Characterizations of MLBXII distribution are also studied via different techniques. Parameters of MLBXII distribution are estimated using the maximum likelihood method. Goodness of fit of this distribution through different methods is studied.
Determinants of Medical Internet of Things Adoption in Healthcare and the Role of Demographic Factors Incorporating Modified UTAUT

—Medical Internet of Things (mIoT) is the IoT sub-set with vast potential in healthcare. However, the adoption of eHealth solutions such as mIoT has been a critical challenge in the health sector of the Kingdom of Saudi Arabia. Therefore, this study was conducted to explore the mIoT adoption determinants in Saudi public hospitals. Methods: A total of 271 participants were recruited from public hospitals in Riyadh, and a modified UTAUT model named UTAUT-HS was developed in this study to test its relevance with respect to mIoT adoption. Results: Ten path relationships were tested in this study, out of which six showed significant results. Similarly, three variables (Computer and English Language Self-efficacy or CESE, Performance Expectancy or PE and Social Influence or SI) showed a significant direct relationship with the behavioural intention to adopt mIoT. Furthermore, CESE showed the strongest relationship and emerged as a major sub-set of Effort Expectancy (EE) for mIoT adoption. However, moderator analysis showed substantial variations between different study demographic groups. In particular, the current study findings unravelled a comparatively novel relevance of Perceived Threat to Autonomy (PTA) for mIoT adoption for clinical and non-clinical and for older and younger participants. Conclusion: The study concludes that UTAUT-HS is an adequate model to explain the mIoT adoption in healthcare. However, it also suggests conducting future large-scale studies in KSA and elsewhere to validate the relevance of UTAUT-HS in other contexts and with much more confidence.
K-NN Classification of Brain Dominance

The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty-one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%.
Adaptive Synchronization of the Identical FitzHugh-Nagumo Chaotic Neuron Models

Chaos is an important applied area in nonlinear dynamical systems and it is applicable to many real-world systems including the biological systems. Nerve membranes are known to exhibit their own nonlinear dynamics which generate and propagate action potentials. Such nonlinear dynamics in nerve membranes can produce chaos in neurons and related bifurcations. In 1952, A.L. Hodgkin and A.F. Huxley proposed a nonlinear dynamical system as a mathematical
SOLVABILITY OF INITIAL PROBLEMS FOR ONE CLASS OF DYNAMICAL EQUATIONS IN QUASI-SOBOLEV SPACES

The equations, which are not solved with respect to the highest derivative, are now actively studied. Such equations are also called the Sobolev type equations. Note that these equations in Banach spaces are studied quite well. Quasi-Sobolev spaces are quasi normalized complete spaces of sequences. Recently these spaces began to be studied. The interest to such spaces and its equations is connected with a desire to fill up the theory more than with practical applications. The paper is devoted to the study of solvability of the Cauchy problem and the Showalter -- Sidorov problem for a class of equations considered in the quasi-Sobolev spases. To this end we use properties of the equation operators, namely the relative boundedness of the operators. To illustrate abstract results we consider an analogue of the Hoff equation in the quasi-Sobolev spaces.
Language and Gender : Implications on the Development of Female Self Concept in Zimbabwe

This article analyzes the relationship between use o f language and gender in the development o f the female self concept. The argument is that language can reinforce asymmetrical gender relations in society through the use o f gender specific vocabulary. Two concepts, that is gender and patriarchy, are discussed and their relationship to the j use of language is shown. Socialist Feminism as the guiding theoretical perspective is discussed as well as Cooley's concept o f the lookingglass self. A detailed analysis o f the t relationship between language and gender is undertaken and in it the following issues are critically discussed: language used about women, language used by women and linguistic differences to social interactions. This article concludes by showing how education can be used as a tool in the deconstruction o f gender stereotypes aboutwomen in the use o f language. The need to avoid the use o f derogatory and demeaning vocabulary in interaction both inside and outside the classroom is emphasized.
LPS exacerbates functional and inﬂammatory responses to ovalbumin and decreases sensitivity to inhaled ﬂuticasone propionate in a guinea pig model of asthma

Asthma exacerbations contribute to corticosteroid insensitivity. LPS is ubiquitous in the environment. It causes bronchoconstriction and airway inﬂammation and may therefore exacerbate allergen responses. This study examined whether LPS and ovalbumin co-administration could exacerbate the airway inﬂammatory and functional responses to ovalbumin in conscious guinea pigs and whether these exacerbated responses were insensitive to inhaled corticosteroid treatment with ﬂuticasone propionate (FP).
Assessing the Hierarchical Hamiltonian Splitting Integrator for Collisionless N-body Simulations

The N-body problem has become one of the hottest topics in the fields of computational dynamics and cosmology. The large dynamical range in some astrophysical problems led to the use of adaptive time steps to integrate particle trajectories, however, the search of optimal strategies is still challenging. We quantify the performance of the hierarchical time step integrator Hamiltonian Splitting (HamSp) for collisionless multistep simulations. We compare with the constant step Leap-Frog (LeapF) integrator and the adaptive one (AKDK). Additionally, we explore the impact of different time step assigning functions. There is a computational overhead in HamSp however there are two interesting advantages: choosing a convenient time-step function may compensate and even turn around the efficiency compared with AKDK. We test both reversibility and time symmetry. The symmetrized nature of the HamSp integration is able to provide time-reversible integration for medium time scales and overall deliver better energy conservation for long integration times, and the linear and angular momentum are preserved at machine precision. We address the impact of using different integrators in astrophysical systems. We found that in most situations both AKDK and HamSp are able to correctly simulate the problems. We conclude that HamSp is an attractive and competitive alternative to AKDK, with, in some cases, faster and with better energy and momentum conservation. The use of recently discussed Bridge splitting techniques with HamSp may allow to reach considerably high efficiency.