Miracle-dz/UniFAR-FTUs huggingface dataset:
MILK YIELD AND COMPOSITION IN BEEF COWS AND THEIR EFFECT ON COW AND CALF PERFORMANCE IN TWO ENVIRONMENTS

Relationships of milk yield and composition of 10 specific F1 crosses of beef cows with preweaning growth of their calves and with changes in the weight and subcutaneous fatness of the cows during lactation were examined over 2 yr for a population of 120 cow-calf pairs each year at two climatically distinct locations. The crosses were represented by the Hereford × Angus (HA) and nine crosses produced by mating Charolais (C), Simmental (S), and Limousin (L) sires to cows of Hereford (H), Angus (A), and Shorthorn (N) breeding. Environmental differences were provided by two climatically distinct locations (Brandon, Manitoba; Manyberries, Alberta) with two treatments (drylot confinement, T1; pasture, T2) imposed at each location. Milk was measured at 8-wk intervals commencing 1 June and terminating 1 October (weaning). Cow weight and fat at calving were positively associated (P < 0.0001) at both locations. Weight of the cow at calving was positively associated with average milk yield and calf growth rate at M.
Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM

Emotional state recognition of a speaker is a difficult task for machine learning algorithms which plays an important role in the field of speech emotion recognition (SER). SER plays a significant role in many real-time applications such as human behavior assessment, human-robot interaction, virtual reality, and emergency centers to analyze the emotional state of speakers. Previous research in this field is mostly focused on handcrafted features and traditional convolutional neural network (CNN) models used to extract high-level features from speech spectrograms to increase the recognition accuracy and overall model cost complexity. In contrast, we introduce a novel framework for SER using a key sequence segment selection based on redial based function network (RBFN) similarity measurement in clusters. The selected sequence is converted into a spectrogram by applying the STFT algorithm and passed into the CNN model to extract the discriminative and salient features from the speech spectrogram. Furthermore, we normalize the CNN features to ensure precise recognition performance and feed them to the deep bi-directional long short-term memory (BiLSTM) to learn the temporal information for recognizing the final state of emotion. In the proposed technique, we process the key segments instead of the whole utterance to reduce the computational complexity of the overall model and normalize the CNN features before their actual processing, so that it can easily recognize the Spatio-temporal information. The proposed system is evaluated over different standard dataset including IEMOCAP, EMO-DB, and RAVDESS to improve the recognition accuracy and reduce the processing time of the model, respectively. The robustness and effectiveness of the suggested SER model is proved from the experimentations when compared to state-of-the-art SER methods with an achieve up to 72.25%, 85.57%, and 77.02% accuracy over IEMOCAP, EMO-DB, and RAVDESS dataset, respectively.
Leucogyrophana (Aphyllophorales): identification of species

The basidiocarps of nine species are described and the cultural characters are presented for six species. All have pale yellow spores and hyphae with clamp connections and primarily occur on coniferous wood where they are associated with a brown rot. Leucogyrophana arizonica and L. romellii are proposed as new species. Merulius pulverulenta Fr. is transferred to Leucogyrophana. New synonymy is proposed following the study of type specimens and several names previously regarded as synonyms are shown to apply to distinct species.
A Mode-localized Tilt Sensor with Resolution of 2.4e-5 Degrees within the Range of 50 Degrees

In this paper, we propose a mode-localized resonant tilt sensor based on 3 degree-of-freedom (DoF) weakly coupled resonators (WCRs). The angle of rotation around the sensing axis causes gravity perturbation to the two outer resonators and influences the energy distribution of the WCRs system, finally resulting in a drastic change of the mode shape. The experimental results indicate that the sensitivity is improved by 326% compared with the 2-DoF mode-localized resonant tilt sensor. Under the closed-loop test, the resolution of the tilt sensor can achieve 2.4e-5 degrees within the range of 50 degrees.
Bacterial Community of the Largest Oligosaline Lake, Namco on the Tibetan Plateau

Bacterial abundances and diversity in the surface water of Lake Namco, the largest oligosaline lake on the Tibetan Plateau, were examined using flow cytometry approach and constructing 16S rRNA gene clone libraries. Bacterial abundances were from 0.08 × 106 to 1.6 × 106 cells mL−1, and were in the reported range of other lakes of the Tibetan Plateau and high mountain regions. Bacterial abundances were significantly correlated with the concentrations of chlorophyll a (chl a), but showed no significant relationship with the dissolved organic carbon (DOC), which suggested that the amount of DOC released by algae was the key factor determining the bacterial abundance rather than the total DOC. The total trace elements concentrations also obviously connected with bacterial abundances, and 9 of 20 elements showed significant relationship. Bacterial 16S rRNA gene clone sequences were affiliated to the α-, β-, γ-, δ-, and ϵ-Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, Acidobacteria, Planctomycetes, Verrucomicrobia, Candidate division OD1, or unclassified, and among these the β-Proteobacteria dominated. Bacteria in Lake Namco were most closely related to those retrieved from freshwater habitats. Relatively few sequences were closely related to those recovered from saline habitats. Eleven of 34 typical freshwater bacterial clusters were detected in the oligosaline Lake Namco. Bacterial diversity within the lake varied and was connected with the concentrations of DOC and chl a.
Effects of volumetric capture avatars on social presence in immersive virtual environments

Recent advances in 3D reconstruction and tracking technologies have made it possible to volumetrically capture human body and performance at real time. In the field of human-computer interaction, however, no works have been reported on the user study made with such volumetric capture avatars. This paper investigates how the volumetric capture avatar affects users’ sense of social presence in immersive virtual environments. In our experiments, the volumetric capture avatar of an actor is compared with the actor captured in 2D video and another 3D avatar obtained by pre-scanning the actor. The experiment results show that users have the highest sense of social presence with the volumetric capture avatar when performing dynamic tasks whereas they have higher sense of social presence with the volumetric capture avatar and 2D video than with the pre-scanned avatar when performing static tasks. These imply that the emerging volumetric capture techniques can be an attractive tool for mixed reality, telepresence, and many other 3D applications.
Home range and seasonal movement of elephants in the Kunene Region, northwestern Namibia

ABSTRACT This paper examines the home ranges and seasonal movements of eight GPS-collared elephants (two females and six males) in the northwestern Kunene region of Namibia. Minimum convex polygon (MCP) and the fixed kernel density estimation (FKDE) methods were used to analyse home ranges. The collared elephants showed defined home and seasonal ranges. In the eastern section of the research area, the elephants generally had smaller home ranges that were at their least during the hot and cold dry seasons, expanding during the wet season. In the western areas, the elephants moved between the Hoanib and Hoarusib Rivers in response to available vegetation that did not necessarily correspond to rainfall. The length of movement of collared elephants varied from 54.5 to 473 km in the eastern section of the research area to between 251 to 625 km in the west, over periods of up to five months.
Towards Accessibility in VR - Development of an Affordable Motion Platform for Wheelchairs

In virtual reality research, extensive effort has been made towards thoughtfully investigating various locomotion and interaction techniques in order to constitute an immersive experience for the user. Mandated by design, seated applications are obliged to utilize alternate means of locomotion due to the inability to naturally move through the environment. Whilst complex devices and techniques are proposed in state-of-the art applications [3, 9], the subset of physically handicapped people is often disregarded by these.
Experimental Verification and Electromagnetic Characteristic Analysis of Permanent Magnet Linear Oscillating Actuator Using Semi 3D Analysis Technique With Corrected Stacking Factor

This study proposes a semi three-dimensional analysis method for a permanent magnet linear device with a radially laminated structure. After the magnetic flux density was calculated using two-dimensional finite element (FE) analysis, the magnetic flux density in the iron core was estimated by considering the volume of the divided core. Finally, a core loss analysis of the laminated core was performed using the modified Steinmetz equation, and the results was compared with the FEM analysis results. Additionally based on the predicted magnetic flux density, the electromagnetic characteristics were compared through the experiment.
Fully Adaptive Ridge Detection Based on STFT Phase Information

This letter deals with the problem of the estimation of the instantaneous frequencies of the modes of multicomponent signals from their linear time-frequency representations. In most approaches, such an estimation consists of extracting the ridges associated with each mode in the time-frequency plane. A major issue associated with these techniques is that ridge detection relies on some ad-hoc parameters which essentially bound the modulation of the studied modes and put some constraints on the type of filter used in the time-frequency representation. In this paper, we alternatively propose a novel fully adaptive approach for ridge detection whose relevance is shown throughout numerical simulations.
A Malware Detection Method for Health Sensor Data Based on Machine Learning

Traditional signature-based malware detection approaches are sensitive to small changes in the malware code. Currently, most malware programs are adapted from existing programs. Hence, they share some common patterns but have different signatures. To health sensor data, it is necessary to identify the malware pattern rather than only detect the small changes. However, to detect these health sensor data in malware programs timely, we propose a fast detection strategy to detect the patterns in the code with machine learning-based approaches. In particular, XGBoost, LightGBM and Random Forests will be exploited in order to analyze the code from health sensor data. The codes are fed into them as sequences of bytes/tokens or just as a single byte/token (e.g. 1-, 2-, 3-, or 4-grams). Terabytes of program with labels, including benign and malware programs, have been collected. The challenges of this task are to select and get the features, modify the three models in order to train and test the dataset, which consists of health sensor data, and evaluate the features and models. When a malware program is detected by one model, its pattern will be broadcast to the other models, which will prevent malware program from intrusion effectively.
Feeding Calcium Salts of Fatty Acids to Lactating Cows

Abstract Digestibility and production responses to feeding Ca salts of fatty acids were determined in lactating cows. Cows 120 d in milk were fed 4% of DM as Ca salts of fatty acids using chromium oxide mordanted corn silage as unabsorbed reference substance. Digestion of DM and N did not change, fatty acid digestion increased, and Ca percentage absorption decreased. Milk and milk fat production were enhanced. In production experiments in Pennsylvania, cows in early lactation were fed 54% of total diet as roughage and . 45 kg/d Ca salts of fatty acids. Milk increased by 4% and FCM by 6%. Plasma somatotropin decreased, and insulin was not changed. In similar experiments in Israel with 31% of total diet as roughage, feeding . 5 kg/d Ca salts of fatty acids to cows following parturition enhanced milk by 5% and FCM by 9.5%. The production increment of Ca salts of fatty acids decreased following peak lactation. No after effects of feeding Ca salts were observed. Body weight changes and rumen VFA were similar, whereas DM intake was . 9 kg less and plasma FFA decreased with feeding Ca salts. It is concluded that inclusion of Ca salts of fatty acids in early lactation enhances production of milk and FCM.
How To Develop Excellent Educational Content for 3D VR

Modern education is progressively encouraging the use of innovative educational platforms. The constant availability of information, regardless of space, and the possibility of online learning are increasingly demanding the development of 3D VR curricula beside e-Learning. This paper examines VR educational materials- produced at Hungarian universities - in terms of spatial awareness, collaboration, -mobile applications, digital fragmentation, digital workflow analysis and cognitive processes. The examination was done on MaxWhere platform. This was the first time VR learning material was developed at a large scale by lecturers who had no knowledge of MaxWhere prior to this work. An important finding of the study is that with the advancement of ED-TECH there is also a change in methodology in education. The purpose of this article is to provide guidelines for developing educational materials-, so that curriculum developers can better understand what kinds of VR learning materials are worth developing and are able to most effectively stimulate the development of cognitive processes.
Regulation of small-scale carbon sink projects in the clean development mechanism from the perspective of aspects of multidisciplinary biodiversity

This paper considers the current regulatory framework of small-scale carbon sinks in the clean development mechanism (CDM). The legal characteristics are assessed from perspectives related to regulation theory. To what extent does CDM regulation support active biodiversity management in the small-scale implementation of afforestation and reforestation projects? After analysing the above question, fresh concepts for better consideration of biodiversity aspects during the implementation of small-scale carbon sink projects in developing countries are presented. These new concepts are examined in the framework of the current process of legal establishment, which seeks to encourage cost-effective project implementation in cases where biodiversity impacts are appropriately taken into account during small-scale projects. Is it possible to create a workable and plausible conceptual set-up for considering additional efforts to preserve and enhance biodiversity while sequestering carbon – and to do it in a way that makes it possible to reward those efforts in the CDM framework? These questions are discussed here in a multidisciplinary manner. It is found that biodiversity specialized carbon management, which includes the new concepts, can be seen as a potential small-scale project framework for maintaining biodiversity and local development in and around a small-scale project area.
Downlink user selection for massive-MIMO-OFDM systems using ZF precoding

By the use of a large number of M antennas in the base station side, makes it possible to beamform different signals to different users, so the signals add up constructively at the desired user and destructively everywhere else, that is why massive Mimo-ODFM combing with beamforming is a promising technology for next generation wireless communication network (5G). Indeed, the performance of Massive MIMO systems is systematically dependent on users' selection method especially when the number of users is huge in the cell. In this paper, we focus on the performance of such a system in terms of max rate using zero forcing precoding method.
Experimental Investigation and Uncertainty Prediction of The Load-Carrying Capacity of Composite Double Hat for Lattice Core Sandwich Panels Using Artificial Neural Network

Carbon fiber reinforced composites are promising candidates for building advanced multifunctional structures with superior properties that are suitable for the next generation of automotive and aircraft applications. This study presents an experimental investigation into the effect of the major orientation of the composite hat section on the crushing behavior and load-carrying capacity of a composite double hat structure. The variation in load carrying capacities due various measurement and manufacturing factors can significantly affect the design and thus safety. Therefore, the uncertainty in load carrying capacities is implicitly considered by identifying the maximum and minimum values of the load-carrying capacities at all displacement values. Artificial neural network-based models are then developed and compared using the Mean Squared Error (MSE) measure, with the objective to predict the load-carrying capacity range at each displacement value, which implicitly considers the uncertainty of results. Three samples of each arrangement are statistically analyzed and utilized in the training. The results show that the ‘X’ hat orientation outperforms the ‘O’ hat orientation in terms of load-carrying capacity. On the contrary, the ‘O’ hat orientation outperforms the ‘X’ hat orientation in terms of crash force efficiency with a total value of 0.6 for the former in comparison to 0.5 for the latter. A two layers ANN-models are found best in terms of performance with total RMSE values of 55.5 N for ‘X’ orientation and 515.8 N for ‘O’ orientation.
Stripline Resonator Loaded Compact SIW Filters With Wide Suppression and Flexible Response

A novel method to design substrate integrated waveguide (SIW) filters with odd-order response is presented in this letter. By combining SIW and stripline technologies, a fully closed, third-order filter consisting of two SIW resonators and one stripline resonator is proposed. The stripline resonator provides a coupling path for the TE101 mode to form a passband, while the couplings for TE102 and TE201 modes are very weak, resulting in the suppression of their propagation. The principle for higher order mode suppression is carefully illustrated. On the basis of this filter, another two filters are designed by introducing different cross couplings, which generate extra transmission zeros (TZs) to enhance the selectivity. These two filters exhibit flexible response with controllable TZs. All these three filters are fabricated and measured. The measured results are in good agreement with the simulation, which validates the proposed design methodology and the idea of incorporating the stripline resonator into the SIW. They demonstrate a low insertion loss, high selectivity, and wide stopband. In addition, the proposed filters are featured by a reduced size due to the applications of the double-layer structure and miniaturized stripline resonators.
The NMDA receptor subunit GluN3A regulates synaptic activity-induced and myocyte enhancer factor 2C (MEF2C)-dependent transcription

N-Methyl-d-aspartate type glutamate receptors (NMDARs) are key mediators of synaptic activity-regulated gene transcription in neurons, both during development and in the adult brain. Developmental differences in the glutamate receptor ionotropic NMDA 2 (GluN2) subunit composition of NMDARs determines whether they activate the transcription factor cAMP-responsive element-binding protein 1 (CREB). However, whether the developmentally regulated GluN3A subunit also modulates NMDAR-induced transcription is unknown. Here, using an array of techniques, including quantitative real-time PCR, immunostaining, reporter gene assays, RNA-Seq, and two-photon glutamate uncaging with calcium imaging, we show that knocking down GluN3A in rat hippocampal neurons promotes the inducible transcription of a subset of NMDAR-sensitive genes. We found that this enhancement is mediated by the accumulation of phosphorylated p38 mitogen-activated protein kinase in the nucleus, which drives the activation of the transcription factor myocyte enhancer factor 2C (MEF2C) and promotes the transcription of a subset of synaptic activity-induced genes, including brain-derived neurotrophic factor (Bdnf) and activity-regulated cytoskeleton-associated protein (Arc). Our evidence that GluN3A regulates MEF2C-dependent transcription reveals a novel mechanism by which NMDAR subunit composition confers specificity to the program of synaptic activity-regulated gene transcription in developing neurons.
Emotional facial perception development in 7, 9 and 11 year-old children: The emergence of a silent eye-tracked emotional other-race effect

The present study examined emotional facial perception (happy and angry) in 7, 9 and 11-year-old children from Caucasian and multicultural environments with an offset task for two ethnic groups of faces (Asian and Caucasian). In this task, participants were required to respond to a dynamic facial expression video when they believed that the first emotion presented had disappeared. Moreover, using an eye-tracker, we evaluated the ocular behavior pattern used to process these different faces. The analyses of reaction times do not show an emotional other-race effect (i.e., a facility in discriminating own-race faces over to other-race ones) in Caucasian children for Caucasian vs. Asian faces through offset times, but an effect of emotional face appeared in the oldest children. Furthermore, an eye-tracked ocular emotion and race-effect relative to processing strategies is observed and evolves between age 7 and 11. This study strengthens the interest in advancing an eye-tracking study in developmental and emotional processing studies, showing that even a “silent” effect should be detected and shrewdly analyzed through an objective means.
Nightly aeration to increase production of channel catfish.

Abstract Channel catfish (Ictalurus punctatus) were stocked in 0.02- and 0.04-hectare ponds at 20,385 fish per hectare and fed daily. Fish averaged 11 cm total length and 6.2 g at stocking. Six ponds received nightly aeration (2 to 6 hours) and six ponds received no aeration. A maximum feeding rate of 90 kg/hectare was reached in October. In aerated ponds, no dissolved oxygen (DO) problems occurred, and survival was 92%. Fish mortalities resulted from DO depletion in all unaerated ponds, and survival was 40%. Nitrite-nitrogen concentrations were significantly higher in the aerated ponds, though never at levels reported as lethal to channel catfish. Concentrations of un-ionized ammonia were high enough in both aerated and unaerated ponds to have adversely affected growth. Aerated ponds yielded an average of 5,307 kg/hectare of channel catfish and had a net economic gain of $1,500 per hectare. The unaerated ponds yielded an average of 1,400 kg/hectare and were an obvious economic failure. Direct extrapolati.