Miracle-dz/UniFAR-FTUs huggingface dataset:
The In Vitro Evaluation of Rooster Semen Pellets Frozen with Dimethylacetamide

Simple Summary Semen cryopreservation remains the most applicable methodology for conserving and transmitting superior genetic backgrounds in poultry species. However, there is wide variability in the results of sperm viability and fertility due to susceptibility to cryodamage. The current study implemented a cryopreservation protocol based on rapid freezing of semen pellets supplemented with various levels of dimethylacetamide (DMA), focusing on in vitro quality, computer-assisted-sperm-analysis (CASA), and anti-freeze-associated gene expression of post-thawed rooster sperm. The current investigation provides novel results regarding the appropriate concentration and molecular mechanism by which DMA could be supplemented to frozen semen pellets and used as a successful protocol for sperm cryopreservation in poultry species. Abstract Sperm cryopreservation is an effective technique for conserving animal genetic diversity and transmitting superior genetic backgrounds, maintained via a non-invasive sampling and collection of huge quantities of sperm. Nevertheless, cryopreservation in avian species is not commercially viable because of the rooster sperm’s susceptibility to damage. This study aims to estimate the impact of dimethylacetamide (DMA) as a cryoprotectant at different levels (3%, 6%, or 9%) on the post-thawed sperm quality, motility, antioxidant-biomarkers, and the expression of anti-freeze related genes. Semen samples were collected twice a week from twelve roosters aged 40 wk, weighing 3400 ± 70 g, and belonging to the Cairo-B2 chicken strain. Fresh semen samples were rapidly appraised, pooled, diluted with two volumes of a basic extender, and divided equally into three groups. The diluted groups were chilled at −20 °C for 7 min, then gently supplemented with 3, 6, or 9% pre-cooled DMA and equilibrated at 5 °C for a further 10 min. Semen pellets were formed by pipetting drops 7 cm above liquid nitrogen (LN2), which were then kept inside cryovials in the LN2. Thawing was performed 2 months later by taking 3–4 pellets of the frozen semen into a glass tube and warming it in a water bath for 8 s at 60 °C. The results showed that 3% DMA increased the proportion of total motile sperm, progressivity, viability, and plasma membrane integrity (%) compared to the 6% and 9% DMA groups. The lipid peroxidation and antioxidant enzyme activity were improved in the 3% group. At the same time, some anti-freeze-related genes’ (including ras homolog family member A (RHOA), heat shock protein 70 (HSP70), and small nuclear ribonucleoprotein polypeptide A (SNRPA1)) expressions were upregulated within the 3% DMA group relative to other groups. In conclusion, the 3% DMA group maintained higher post-thawed sperm quality than the other tested groups.
Geometric aspects of transformations of forces acting upon a swimmer in a3-D incompressible fluid

Our goal in this paper is to investigate how the geometric shape of a swimmer affects the forces acting upon it in a 3-$D$ incompressible fluid, such as governed by the non-stationary Stokes or Navier-Stokes equations. Namely, we are interested in the following question: How will the swimmer's internal forces (i.e., not moving the center of swimmer's mass when it is not inside a fluid) ``transform'' their actions when the swimmer is placed into a fluid (thus, possibly, creating its self-propelling motion)? We focus on the case when the swimmer's body consists of either small parallelepipeds or balls. Such problems are of interest in biology and engineering application dealing with propulsion systems in fluids.
Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods’ performance and DM signatures’ quality in the absence of gold standard data solving a long-existing problem in DM analysis.
Generalised tanh-shaped hyperbolic potential: Klein–Gordon equation's bound state solution

The development of potential theory heightens the understanding of fundamental interactions in quantum systems. In this paper, the bound state solution of the modified radial Klein–Gordon equation is presented for generalised tanh-shaped hyperbolic potential from the Nikiforov–Uvarov method. The resulting energy eigenvalues and corresponding radial wave functions are expressed in terms of the Jacobi polynomials for arbitrary l states. It is also demonstrated that energy eigenvalues strongly correlate with potential parameters for quantum states. Considering particular cases, the generalised tanh-shaped hyperbolic potential and its derived energy eigenvalues exhibit good agreement with the reported findings. Furthermore, the rovibrational energies are calculated for three representative diatomic molecules, namely H2, HCl and O2. The lowest excitation energies are in perfect agreement with experimental results. Overall, the potential model is displayed to be a viable candidate for concurrently prescribing numerous quantum systems.
HIGH-DIMENSIONAL DATA ASSIMILATION AND MORPHING ENSEMBLE KALMAN FILTERS WITH APPLICATIONS IN WILDFIRE MODELING

In this dissertation, we examine practical aspects of incorporating data into highdimensional and nonlinear models. The Kalman filter is discussed from a theoretical prospective and is shown to solve the Bayesian data assimilation problem under certain linearity and normality conditions. The ensemble Kalman filter (EnKF) and its variants are presented as an approximation to the optimal filtering distributions using statistical sampling that are used even when the assumptions of the Kalman filter are violated. We present a series of numerical experiments that explore the behavior of the EnKF as the model state size becomes large. Finally, we present an application, wildfire modeling, in which the standard EnKF techniques fail. The behavior of the EnKF applied to this highly nonlinear problem inspires the development of two new types of ensemble filters: predictor-corrector and morphing. Predictor-corrector filters use a proposal ensemble (obtained by some method, called the predictor) with assignment of importance weights to recover the correct statistic of the posterior (the corrector). The proposal ensemble comes from an arbitrary unknown distribution and only needs concentrated coverage of the support of the posterior. The ratio of the prior and the proposal densities for calculating the importance weights is obtained by density estimation. The prediction created by the ensemble Kalman formulas and correction formed by nonparametric density estimation based on the distance in Sobolev spaces are considered. Numerical experiments show that the new predictor-corrector filters combine the advantages of ensemble Kalman filters and particle filters for highly nonlinear systems and that they are suitable for high dimensional states that are discretizations of solutions of partial differential equations. The morphing filter combines the EnKF with the ideas of morphing and registration from image processing. This results in filters suitable for nonlinear problems in which the solutions exhibit moving coherent features, such as thin interfaces in wildfire modeling. The ensemble members are represented as the composition of one common state with a spatial transformation, called registration mapping, plus a residual. A fully automatic registration method is used that requires only gridded data, so the features in the model state do not need to be identified by the user. The morphing EnKF operates on a transformed state consisting of the mapping obtained through registration and the residual image. The EnKF is shown to perform far better when applied to this transformed state than to the original model state for a wildfire model containing spatial errors. This abstract accurately represents the content of the candidate’s thesis. I recommend its publication.
Dietary Coated Sodium Butyrate Ameliorates Hepatic Lipid Accumulation and Inflammation via Enhancing Antioxidative Function in Post-Peaking Laying Hens

During the aging process of laying hens, hepatic oxidative stress damage and lipid accumulation are prone to occur, leading to the deterioration of egg quality and a decline in production properties. This research was designed to explore the effects of different levels of coated sodium butyrate (CSB) addition on oxidation resistance, inflammatory reaction, lipid metabolism and hepatic oxidative damage-related gene expression in aged laying hens. A total of 720 healthy 52 weeks old Huafeng laying hens were arbitrarily divided into 5 groups of 6 replicates with 24 birds each and fed a basal diet supplemented with 0, 250, 500, 750 and 1000 mg/kg CSB for 8 weeks, respectively. The CSB quadratically upgraded GSH-Px activities and downgraded MDA content in the liver and serum. The LDL-C, NEFA and TG contents decreased quadratically in CSB groups and significantly reduced the fatty vacuoles as well as the formation of fat granules in the liver (p < 0.05). Meanwhile, the CSB quadratically upregulated the gene expression of IL-10, Nrf2 and HO1, but downregulated the gene expression of IFN-γ, TNF-α and Keap1 in a quadratic manner (p < 0.05). Moreover, the CSB quadratically degraded the mRNA level of fatty acid synthesis but increased the gene level of key enzymes of fatty acid catabolism (p < 0.05). In conclusion, dietary CSB supplementation has a favorable effect in protecting against liver injury and alleviating lipid accumulation and inflammation by enhancing hepatic antioxidative function in aged laying hens.
Will various interpretation strategies of the same ultrahigh‐resolution mass spectrometry data tell different biogeochemical stories? A first assessment based on natural aquatic dissolved organic matter

Fourier ion cyclotron resonance mass spectrometry (FT‐ICR MS), one of the state‐of‐the‐art ultrahigh‐resolution techniques, is widely used in dissolved organic matter (DOM) research. As research that focuses on identifying DOM molecular fingerprints increases tremendously, there is and will be an urgent need to compare among studies. Different research groups usually use various types of data processing and interpretation strategies. It is critical to explore if different interpretation strategies will impact the comparability of FT‐ICR MS results and their biogeochemical interpretations. To address this question, we selected DOM samples along a freshwater‐to‐marine continuum to be measured by negative‐ion mode electrospray ionization FT‐ICR MS. We interpreted the raw MS data using different strategies, compared the results and evaluated the interpretation strategy‐induced effects on biogeochemical interpretations. Our results show that a total of 3827 formulas that account for 91.6% ± 4.1% (on average) of the total intensity are assigned in all interpretation strategies, while 6521 formulas for 8.4% ± 4.1% (on average) of the total intensity are not commonly assigned in all three interpretation strategies. We conclude that (i) different interpretation strategies do not significantly affect the geochemical stories relied on DOM molecular composition, and (ii) comparison based on intensity results gives more reliable results than the formular number alone. Moreover, we also provide raw and interpreted data (by our different strategies) for the community to compare their interpretation. We aim to call attention to the community for improving the comparability of FT‐ICR MS results and facilitating the integration of DOM molecular composition among global studies.
ACCURACY OF CLOSE-RANGE ANALYTICAL RESTITUTIONS: PRACTICAL EXPERIMENTS AND PREDICTIONS

Some essential points relating to precision and accuracy (distinction between precision and accuracy, correct evaluation of accuracy) are reviewed; then, based on experimental results from several sources, a quantitative study of the accuracy of analytical restitutions in the case of the photo-pair is presented. The principal themes are the following: The effect of measurement redundance upon the accuracy (repetition of the settings, use of several neighboring targets to define an object point, and use of several frames at each station can on an average increase the accuracy by 50 per cent, whatever be the base-to-mean-object-distance ratio, the maximal accuracy for a certain kind of photogrammetric system has been found to correspond to a measurement equivalent normal law with a 1.2 micrometers standard deviation (which is the RMS bias of the measurements) and the minimum accuracy to a normal law with a 2.5 micrometers standard deviation); The effect of the geometrical characteristics of the system (base-to-object-distance ratio, camera asis convergence, and number and disposition of the control points); Accuracy prediction (two predictors are presented; the Karara/Abdel Aziz predictor, which reduces the problem to the central point of the object volume, and a predictor obtained from simulation. These predictors are correct on the condition that a good estimation of the standard deviation of the equivalent normal law of the comparator observation of the system (camera plus comparator) is employed; and Non-metric camera accuracy (for the best of them, it seems to be the same as for metric cameras).
A Joint Speech Enhancement and Self-Supervised Representation Learning Framework for Noise-Robust Speech Recognition

Though speech enhancement (SE) can be used to improve speech quality in noisy environments, it may also cause distortions that degrade the performance of automatic speech recognition (ASR) models. Self-supervised pre-training, on the other hand, has been shown to improve the noise robustness of ASR models. However, the potential of the (optimal) integration of SE and self-supervised pre-training still remains unclear. In this paper, we propose a novel self-supervised pre-training framework that incorporates SE to improve ASR performance in noisy environments. First, in the pre-training phase the original noisy waveform or the waveform obtained by SE is fed into the self-supervised model to learn the contextual representation, where the quantized clean speech acts as the target. Second, we propose a dual-attention fusion method to fuse the features of noisy and enhanced speech, which can compensate for the information loss caused by separately using individual modules. Due to the flexible exploitation of clean/noisy/enhanced branches, the proposed method turns out to be a generalization of some existing noise-robust ASR models, e.g., enhanced wav2vec2.0. Finally, experimental results on both synthetic and real noisy datasets show that the proposed joint training approach can improve the ASR performance under various noisy settings, leading to a stronger noise robustness.
The Antecedents of Consumer Satisfaction Toward OTT Platforms During COVID-19 Lockdown in India

The outbreak of COVID-19 has emerged as the biggest threat to human life. It has changed the entire lifestyle of human beings concerning their emotional stability and cognitive development. Enjoyment is one of the emotions and acts as a positive stimulus that a consumer used to feel and seeks for a balanced life. Since the nationwide lockdown was implemented on March 25, 2020, people have turned restless about how to enjoy themselves at home, as all of their options of going out were being shattered. It was the time when continuous innovations in the form of digital content through over-the-top (OTT) platforms flourished and provided an affordable and diversified entertainment source to consumers. These OTT services help consumers to view the contents via the Internet directly. Furthermore, it has drastically changed people’s preferences toward diversified content based on their choices. This study aims to explore the various determinants affecting consumer satisfaction toward the OTT platforms amid COVID-19 crisis in India. The study’s main findings revealed that among the demographic variables, age group of the consumer largely influences their satisfaction toward OTT platforms. Moreover, work from home, affordability, convenience, and content quality are the significant determinants affecting consumer satisfaction levels toward OTT platforms. The study is relevant to the current marketing scenario, as it provides useful insights for the content developers of the major OTT platforms such as Amazon Prime, Netflix, Disney+ Hotstar, Voot, and many others to enhance the consumer satisfaction in the terms of digital content consumption.
AN EFFICIENT STOCHASTIC SEARCH WITH MINIMAL INITIAL POPULATION FOR STRUCTURAL OPTIMIZATION

Genetic Algorithms are best suited for unconstrained problems; however, most of the practical cases have constraints. As a common approach, modifying initial population due to problem-specific information has not yet come to an end. This is due to the generalization challenges and also the lack of diversity and effectiveness regarding relatively narrow size of the feasible subspace of the entire search space. In this article, a new type of expanding genetic population is presented starting from its minimal size. Suitable ideas from ant colony and simulated annealing approaches are utilized for an adaptive efficient search which is also tuneable by the developed extra control parameters. Effectiveness and efficiency of the proposed method are illustrated by capturing the global optimum in a number of well-known structural size and layout optimization examples in a considerably less fitness evaluations compared to the other standard methods.
Signal Integrity Analysis of Neuronal Spike Signal in 3-D Packaging

Prompted by the continual advancements in artificial intelligence, the neuromorphic chip based on a spiking neural network (SNN) has attracted considerable attention because of its beneficial architecture of memory computing integration. Unlike traditional artificial neural networks, SNNs process information based on discrete-time spikes. This unique spike signal tends to bring an entire new series of signal integrity (SI) problems in three-dimensional (3-D) packaging. In this article, the resistance–inductance–capacitance–conductance (RLGC) equivalent circuit of through-silicon vias (TSV) and redistribution layer (RDL) structure was modeled in 3-D packaging. Furthermore, the spike SI issues, such as reflection, delay, and loss of spike signals, were also analyzed in 3-D packaging. The results illustrated that the corners between RDL and TSV in 3-D packaging could lead to reflections on the spike signals, resulting in distorted waveforms and increased signal loss. The time delay of the spike signal is only related to the electrical characteristics of the transmission link itself and not to the input signal. In addition, the SI of the spike signal was simulated with possible internal voids as well as the open and short defects in the 3-D packaging. The findings also demonstrated that both open and short defects distort the spike signal's waveform, whereas internal voids almost do not affect the signal. This article presents the first systematic analysis of numerous SI issues of spike signals in 3-D packaging while providing a specific reference for designing neuromorphic chips.
Soil amendments impact root‐associated fungal communities of balsam poplar on a phosphogypsum reclamation site

During reclamation organisms like fungi directly interact at the soil–plant interface and can potentially impact plant growth, health, and overall revegetation success. Therefore, a study was conducted to determine how topsoil amendments (mycorrhizae, biochar, compost, manure) influence root‐associated fungal communities and growth of fast growing Populus balsamifera trees on a phosphogypsum (PG) reclamation site in Alberta, Canada. Tree height, diameter, biomass, and diversity of Ascomycota and Basidiomycota were assessed using Illumina MiSeq sequencing targeting the fungal internal transcribed spacer region 1 (ITS1) and ITS2. Topsoil amendments and root location (topsoil vs. PG within a treatment), greatly affected the root fungal community. Basidiomycota preferentially grew in PG, and had a high treatment specific root fungal community with highest diversity of ectomycorrhizal fungi and basidiomycete yeast in biochar PG and compost PG. Populus balsamifera roots were associated with a high diversity of Ascomycota, found equally in roots in topsoil and PG, with a trend towards higher diversity and abundance in PG. The core genus found across all samples was Fusarium, a potential plant pathogen. All topsoil amendments strongly reduced abundance of Fusarium and other Ascomycota in poplar roots. Biochar and compost reduced number and abundance of potential fungal pathogens and increased number of potential plant growth promoting Basidiomycota, indicating favorable growth conditions for P. balsamifera on a phosphygypsum reclamation site by application of these amendments. This study indicates compost and biochar can be effective soil amendments to support P. balsamifera establishment and growth on a PG reclamation site.
Joint Real-Time Identification for Mutual Inductance and Load Charging Parameters of IPT System

Accurate and fast identification for mutual inductance and load charging parameters of inductive wireless power transfer (IPT) system is essential for impedance matching, and the output control of a communication-free IPT system. A joint real-time identification method for mutual inductance and load charging parameters of the IPT system is proposed in this article. Only via measuring the rms value of fundamental voltage and current on the primary side, the double equivalent impedance modulus equations about mutual inductance and equivalent load resistance are established. To obtain real-time identification results of mutual inductance and equivalent load resistance from the high-order implicit function equations, an improved adaptive filtering algorithm based on least mean square (LMS) is proposed. Then, the identification values of load charging parameters are obtained according to the identified mutual inductance and equivalent load resistance. Finally, a 3.6-kW platform is built to verify the validity of the proposed method. And the experimental results show that, compared with other existing achievements, the proposed method can identify <inline-formula> <tex-math notation="LaTeX">${U}_{\mathbf {Bat}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${I}_{\mathbf {Bat}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${R}_{\mathbf {L}}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> simultaneously in real-time without changing the system operation, and it has a faster rate and higher accuracy of identification.