Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of genetic parameters for dairy ewes using random regression animal model.

The objective of the study was to compare different models in the estimation of genetic parameters for test-day milk records in Churra da Terra Quente (CTQ) ewes. Data comprising 10700 test-day measurements from the first lactation of 3096 ewes were used in analysis of morning milk yield (MMY), afternoon milk yield (AMY), and daily milk yield (DMY). Records before 30 and after 150 days in milk were discharged. Average milk yield was 217.1 g, 198.3 g, and 415.4 g for MMY, AMY, and DMY, respectively. Pedigree le contained 5494 animals. Simple fixed regression animal model (FRM) and random regression animal models (RRM), where orthogonal Legendre polynomials of order 3 were used. The REML method as implemented in the VCE-5 programme was applied for estimation of (co)variance components. Statistical models contained linear regression on days in milk and number of lambs born as fixed effects, while fock-test-day, permanent environment of a ewe, and direct additive genetic effect were treated as random. Estimates of heritability from FRM were low, from 2.9% for AMY to 8.2% for MMY. Heritability estimates from RRM presented a maximum at 60 days of lactation (≥ 33.9%) and decreased to values lower than 2.5%at the end of lactation. There is a potential for using random regression to model additive genetic and permanent environmental effects for genetic evaluation in CTQ ewes, especially from the first two thirds of lactation when decision on mating has to be taken.
Position sensorless direct torque control for six‐phase permanent magnet synchronous motor under two‐phase open circuit

A position sensor is usually required in the existing fault-tolerant direct torque control (FT-DTC) system for the multiphase permanent magnet synchronous motor (PMSM). Thus, the volume, weight, and cost of the system is increased, and the reliability is decreased. The conventional high-performance position sensorless strategies for the multiphase motor under fault-free condition cannot be directly applied to FT-DTC system. To improve this situation, a high-speed position sensorless FT-DTC strategy for a six-phase symmetrical winding PMSM with two opened phases is proposed in this study. Firstly, modified variables and a new rotating coordinate system are defined to achieve the symmetrical and decoupled mathematical model. Secondly, a stator flux observer based on the modified variables and a new coordinate system is proposed. According to the current model of modified stator flux, the estimated modified stator current can be achieved. Then, the estimated error of the modified stator current is sent to the integrator of the modified stator flux observer to correct the voltage model. Finally, the proposed stator flux observer is applied in FT-DTC for six-phase PMSM with two opened phases to realize the position sensorless operation at high speed. The experimental results verify the feasibility and effectiveness of the proposed strategy.
Microwave versus Conventional Sintering of NiTi Alloys Processed by Mechanical Alloying

The present study shows a comparison between two sintering processes, microwave and conventional sintering, for the manufacture of NiTi porous specimens starting from powder mixtures of nickel and titanium hydrogenation–dehydrogenation (HDH) milled by mechanical alloying for a short time (25 min). The samples were sintered at 850 °C for 15 min and 120 min, respectively. Both samples exhibited porosity, and the pore size results are within the range of the human bone. The NiTi intermetallic compound (B2, R-phase, and B19′) was detected in both sintered samples through X-ray diffraction (XRD) and electron backscattering diffraction (EBSD) on scanning electron microscopic (SEM). Two-step phase transformation occurred in both sintering processes with cooling and heating, the latter occurring with an overlap of the peaks, according to the differential scanning calorimetry (DSC) results. From scanning electron microscopy/electron backscatter diffraction, the R-phase and B2/B19′ were detected in microwave and conventional sintering, respectively. The instrumented ultramicrohardness results show the highest elastic work values for the conventionally sintered sample. It was observed throughout this investigation that using mechanical alloying (MA) powders enabled, in both sintering processes, good results, such as intermetallic formation and densification in the range for biomedical applications.
Is Repetition Really the Key to Success? The Impact of Ad Repetition and the Power of “Likes” on Facebook

Abstract In today’s marketplace, social media advertising, such as Facebook ads, are an especially valuable tool for small and medium-sized enterprises (SMEs), which are often financially volatile due to their limited access to resources, as well as political, economic, and social factors. The purpose of this article is to help SMEs better understand how to develop an advertising strategy on Facebook that is mindful of their time and resources. The findings of this article suggest that social media ad frequency, specifically on Facebook, leads to website visits, and social media engagement metrics (i.e., post shares and comments) can strengthen this relationship. Website visits, in turn, are suggested to increase the likelihood of purchases made during visits, and longer durations of visits can strengthen this relationship. This research contributes to our knowledge of Facebook advertising effects by examining behavioral trace data generated by consumers engaging with SMEs. By examining real-time consumption patterns, this research provides immediate managerial implications that can be employed by SMEs as they develop Facebook marketing plans for the future.
Traffic Scenario Clustering and Load Balancing with Distilled Reinforcement Learning Policies

Due to the rapid increase in wireless communication traffic in recent years, load balancing is becoming increasingly important for ensuring the quality of service. However, variations in traffic patterns near different serving base stations make this task challenging. On one hand, crafting a single control policy that performs well across all base station sectors is often difficult. On the other hand, maintaining separate controllers for every sector introduces overhead, and leads to redundancy if some of the sectors experience similar traffic patterns. In this paper, we propose to construct a concise set of controllers that cover a wide range of traffic scenarios, allowing the operator to select a suitable controller for each sector based on local traffic conditions. To construct these controllers, we present a method that clusters similar scenarios and learns a general control policy for each cluster. We use deep reinforcement learning (RL) to first train separate control policies on diverse traffic scenarios, and then incrementally merge together similar RL policies via knowledge distillation. Experimental results show that our concise policy set reduces redundancy with very minor performance degradation compared to policies trained separately on each traffic scenario. Our method also outperforms handcrafted control parameters, joint learning on all tasks, and two popular clustering methods.
Effect Of Microfinance Bank’s Credit On Cereal Crops Productivity In Federal Capital Territory, Abuja

This study was carried out to examine the effect of microfinance bank‘s credit on Cereal crop productivity in federal capital territory Abuja, Nigeria. Three Area Councils were usedBwari, Gwagwalada, and Kuje. Specifically, the study described the socio-economic characteristic of cereal crop farmer users and non-users examine the effect of microfinance banks‘ credit on productivity of users and non-users of cereal farmers, estimate the profitability of cereal crop production, and identify the major constraints associated with microfinance banks‘ credit. Multi-stage Sampling technique was used to select a total of 180 respondent including 90 users and 90 nonusers of microfinance banks credit were randomly selected for the Survey. The sample size was drawn from three microfinance bank namely Hasal, Fortis, and Atlas. Both primary and secondary data were collected for the study. Primary data were obtained through questionnaires administered to the respondent. Indices used in data analysis include descriptive statistic, multiple regression analyses, the socio-economic characteristic of the respondent were analyzed using. The data were subjected to both descriptive and inferential statistical analysis. The result shows that 41% of the respondents were between the ages 3140 years, 77% were male, 49% had farming experience of between 11-20 years, 56% had family sizes of 1 – 5 persons and 93% had formal education at various levels. The coefficients of cost of farm size, labour, seed, capital input and amount of credit were all positive and significant at 1%, 5%, 1%, 1% and 1% level of probability respectively. It was concluded that users of microfinance bank‘s credit were more efficient than the nonusers of microfinance bank‘s credit in the study area. The study identified Bureaucratic procedure, high interest rate, and frequent absences of loan officer, among others as the most pressing constraints encountered by respondent in accessing the loan. Keyword: Agriculture, Cereal, Credit, Crop, Farmers, Microfinance, and Productivity, ————————————————————
Lectures on quantum computing, thermodynamics and statistical physics

This book is a collection of lecture notes from the Symposium on Quantum Computing, Thermodynamics, and Statistical Physics, held at Kinki University in March 2012. Quantum information theory has a deep connection with statistical physics and thermodynamics. This volume introduces some of the topics on interface among the mentioned fields. Subjects included in the lecture notes include quantum annealing method, nonequilibrium thermodynamics and spin glass theory, among others. These subjects were presented with much emphasis put in its relevance in quantum information theory. These lecture notes are prepared in a self-contained manner so that a reader with modest background may understand the subjects.
Novel rock mineral fertiliser application with microbial consortium inoculant enhances growth, yield and grain protein content of wheat (Triticum aestivum L.) in sandy soil

ABSTRACT Grain yield and protein concentration are critical traits determining wheat economic value and are dependent on nitrogen (N) supply and crop N status. However, application of N fertilisers is costly and has a large environmental footprint. We assessed the effect of slow-release rock mineral fertiliser (RMF) with or without microbial consortium inoculant (MI) on growth, grain yield and protein content of winter wheat grown in a low-fertility soil. The experiment was set up with three factors [RMF rates (0, 10 and 20 mg N kg−1 soil), MI (with and without) and two wheat genotypes] replicated four times and harvested twice (anthesis and maturity). The addition of MI increased root biomass at anthesis, but there was no effect at maturity. The shoot and grain nutrient content and protein yield were highest in plants treated with 20 mg N kg−1 soil. The plants treated with MI resulted in lower grain protein concentration than without MI. Arbuscular mycorrhizal (AM) colonisation was higher in treatment with than without MI. The application of RMF at 20 mg N kg−1 soil produced higher plant growth, nutrient content, grain and protein yield compared to control in nutrient-poor sandy soil.
Feasibility and Comparative Effectiveness for the Delivery of the National Diabetes Prevention Program through Cooperative Extension in Rural Communities

The U.S. Cooperative Extension Service (CE) has potential to deliver the National Diabetes Prevention Program (NDPP) to rural residents with prediabetes. However, the CE remains underutilized for the delivery of NDPP. We compared the feasibility/effectiveness of the NDPP (0–6 mos.) delivered by CE personnel to rural residents with prediabetes using Zoom® (CE-Zoom®) or by our research staff using Facebook® (FB). Adults (n = 31, age ~55 years) were enrolled (CE-Zoom® n = 16, FB n = 15). Attendance did not differ significantly between groups (CE Zoom® = 69%, FB = 83%, p = 0.15). Participant retention was similar in the CE Zoom® (88%) and FB groups (87%). CE-Zoom® and FB® groups provided weekly, self-monitoring data for 83% and 84% of the 24 potential weeks, respectively. Six-month weight loss was not different between groups (CE-Zoom® = −5.99 ± 8.0 kg, −5.4%, FB = −1.68 ± 3.3 kg, −1.6% p = 0.13). Participants achieving ≥5% weight loss was greater in the CE-Zoom® (44%) compared with the FB group (7%, p = 0.04). Participants achieving the NDPP program goal for physical activity (≥150 min/week) did not differ (CE-Zoom® = 75%, FB = 67%, p = 0.91). This pilot trial demonstrated the potential feasibility and effectiveness of the NDPP delivered by CE personnel in a group remote format (Zoom®) to adults with prediabetes living in rural areas.
Explicit birational geometry of 3-folds

Foreword 1. One parameter families containing three dimensional toric Gorenstein singularities K. Altmann 2. Nonrational covers of CPm x CPn J. Kollar 3. Essentials of the method of maximal singularities A. V. Pukhlikov 4. Working with weighted complete intersections A. R. Iano-Fletcher 5. Fano 3-fold hypersurfaces A. Corti, A. V. Pukhlikov and M. Reid 6. Singularities of linear systems and 3-fold birational geometry A. Corti 7. Twenty five years of 3-folds - an old person's view M. Reid.
Videoscope-based inspection of turbofan engine blades using convolutional neural networks and image processing

A typical aircraft engine consists of fans, compressors, turbines, and so on, and each is made of multiple layers of blades. Discovering the site of damages among the large number of blades during aircraft engine maintenance is quite important. However, it is impossible to look directly into the engine unless it is disassembled. For this reason, optical equipment such as a videoscope is used to visually inspect the blades of an engine through inspection holes. The videoscope inspection method has some obvious drawbacks such as the long-time attention on microscopic video feed and high labor intensity. In this research, we developed a damage recognition algorithm using convolutional neural networks and some image-processing techniques related to feature point extraction and matching in order to improve the videoscope inspection method. The image-processing techniques were mainly used for the preprocessing of the videoscope images, from which a suspected damaged region is selected after the preprocessing. The suspected region is finally classified as damaged or normal by the pre-trained convolutional neural networks. We trained the convolutional neural networks 2000 times by using data from 380 images and calculated the classification accuracy using data from 40 images. After repeating the above procedure 50 times with the data randomly divided into training and test groups, an average classification accuracy of 95.2% for each image and a damage detectability of 100% in video were obtained. For verification of the proposed approach, the convolutional neural network part was compared with the traditional neural network, and the preprocessing was compared with the region proposal network of the faster region–based convolutional neural networks. In addition, we developed a platform based on the developed damage recognition algorithm and conducted field tests with a videoscope for a real engine. The damage detection AI platform was successfully applied to the inspection video probed in an in-service engine.
Diffusion and Reactions in Fractals and Disordered Systems: Finite reaction rates

Preface Part I. Basic Concepts: 1. Fractals 2. Percolation 3. Random walks and diffusion 4. Beyond random walks Part II. Anomalous Diffusion: 5. Diffusion in the Sierpinski gasket 6. Diffusion in percolation clusters 7. Diffusion in loopless structures 8. Disordered transition rates 9. Biased anomalous diffusion 10. Excluded-volume interactions Part III. Diffusion-Limited Reactions: 11. Classical models of reactions 12. Trapping 13. Simple reaction models 14. Reaction-diffusion fronts Part IV. Diffusion-Limited Coalescence: An Exactly Solvable Model: 15. Coalescence and the IPDF method 16. Irreversible coalescence 17. Reversible coalescence 18. Complete representations of coalescence 19. Finite reaction rates Appendix A. Fractal dimension Appendix B. Number of distinct sites visited by random walks Appendix C. Exact enumeration Appendix D. Long-range correlations References Index.
A method for quantifying how the activity of an enzyme is affected by the net charge of its nearest crowded neighbor

The electrostatic effects of protein crowding have not been systematically explored. Rather, protein crowding is generally studied with co‐solvents or crowders that are electrostatically neutral, with no methods to measure how the net charge (Z) of a crowder affects protein function. For example, can the activity of an enzyme be affected electrostatically by the net charge of its neighbor in crowded milieu? This paper reports a method for crowding proteins of different net charge to an enzyme via semi‐random chemical crosslinking. As a proof of concept, RNase A was crowded (at distances ≤ the Debye length) via crosslinking to different heme proteins with Z = +8.50 ± 0.04, Z = +6.39 ± 0.12, or Z = −10.30 ± 1.32. Crosslinking did not disrupt the structure of proteins, according to amide H/D exchange, and did not inhibit RNase A activity. For RNase A, we found that the electrostatic environment of each crowded neighbor had significant effects on rates of RNA hydrolysis. Crowding with cationic cytochrome c led to increases in activity, while crowding with anionic “supercharged” cytochrome c or myoglobin diminished activity. Surprisingly, electrostatic crowding effects were amplified at high ionic strength (I = 0.201 M) and attenuated at low ionic strength (I = 0.011 M). This salt dependence might be caused by a unique set of electric double layers at the dimer interspace (maximum distance of 8 Å, which cannot accommodate four layers). This new method of crowding via crosslinking can be used to search for electrostatic effects in protein crowding.
BEM OTFS Receiver with Superimposed Pilots over Channels with Doppler and Delay Spread

In this paper, a near-optimal Karhunen-Loeve basis expansion modeling (KL-BEM) orthogonal time frequency space (OTFS) receiver with superimposed pilots has been proposed for high-mobility communications over time-varying channels with Doppler and delay spread. First, an initial KL-BEM channel estimation is conducted using superimposed pilots, followed by the removal of superimposed pilots from the received OTFS signal and equalization based on message passing (MP) algorithm. After that, the detected data symbols are utilized as pseudo pilots together with the superimposed pilots to refine both KL-BEM channel estimation and equalization in an iterative manner. Simulation results confirm the superior performance of the proposed KL-BEM OTFS receiver over prior art in terms of bit error rate (BER). The resulting BER performance is close to the lower bound obtained by assuming perfect channel estimation. Besides, the proposed scheme provides high spectral efficiency, while featuring fast convergence and affordable complexity.
Analytical Modeling of 3D NAND Flash Cell With a Gaussian Doping Profile

The incessantly increasing demand for highly dense storage medium in this era of big-data has led to the development of 3D NAND Flash memories. 3D NAND Flash based SSDs have revolutionized edge storage and become an integral part of the data warehouses and the cloud storage systems. The conventional 3D NAND flash memory with greater than hundred stacked word-line (WL) layers suffer from channel tapering and non-uniformity in the threshold voltage of cells in different WL layers which necessitates the use of different programming voltages for different WL layers and a complex error-correction circuitry. A non-uniform vertical (Gaussian) channel doping profile alleviates the threshold voltage non-uniformity and leads to a significant reduction in the complexity of the error-correction circuitry and a simple (uniform) programming scheme for all WLs. Although behavioral models have been proposed to utilize 3D NAND flash memory for circuit and system-level applications, an analytical model is important to understand the intricate details of the device physics and propose design guidelines for efficient cell design. To this end, in this paper, for the first time, we have proposed an analytical model for the characteristic length, surface potential and inner potential of the Macaroni-body 3D NAND flash cell with vertical Gaussian doping profile. A strong agreement between the analytical model and the TCAD simulations for different gate lengths (down to 25 nm), inner and outer radius, channel and oxide thickness of the 3D NAND flash cell validates the efficacy of the developed model.
Experimental Infection of Peromyscus Species Rodents with Sin Nombre Virus

We demonstrate that 6 distinct Peromyscus rodent species are permissive to experimental infection with Sin Nombre orthohantavirus (SNV). Viral RNA and SNV antibodies were detected in members of all 6 species. P. leucopus mice demonstrated markedly higher viral and antibody titers than P. maniculatus mice, the established primary hosts for SNV.
Development of Seedless Orange and Grapefruit Cultivars through Seed Irradiation

Seed of ‘Pineapple’ orange [Citrus sinensis (L.) Osbeck] and ‘Duncan’ and ‘Foster’ grapefruit (C. paradisi Macf.) were exposed to gamma rays at 10, 15, 20, 25, and 30 krad. Seedling emergence was delayed. LD50 levels were 10-15 krad for ‘Pineapple’, 15 krad for ‘Duncan’, and ≤10 krad for ‘Foster’. The greater sensitivity of ‘Foster’ may have been due in part to higher seed moisture content at treatment. Seedless mutants were obtained following gamma irradiation and fruiting of small numbers of seed and seedlings. The frequency of seedless mutants of ‘Pineapple’ and ‘Duncan’ was highest following treatments of 20-25 krad.
Bus Single-Trip Time Prediction Based on Ensemble Learning

The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examined as the base models, and three ensemble models are further constructed by using various ensemble methods including Random Forest (bagging), AdaBoost (boosting), and Linear Regression (stacking). A data-driven bus single-trip time prediction framework is then proposed, which consists of three phases including traffic data analysis, feature extraction, and ensemble model prediction. Finally, the data features and the proposed ensembled models are analyzed using real-world datasets that are collected from the Beijing Transportation Operations Coordination Center (TOCC). Through comparing the predicting results, the following conclusions are drawn: (1) the accuracy of predicting by using the three ensemble models constructed is better than the corresponding prediction results by using the five sub-models; (2) the Random Forest ensemble model constructed based on the bagging method has the best prediction accuracy among the three ensemble models; and (3) in terms of the five sub-models, the prediction accuracy of LR is better than that of the other four models.