Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of different levels of dietary crude protein on the physiological response, reproductive performance, blood profiles, milk composition and odor emission in gestating sows

Objective This study was conducted to evaluate the effects of crude protein (CP) levels on the physiological response, reproductive performance, blood profiles, milk composition and odor emission in gestating sows. Methods Seventy-two multiparous sows (Yorkshire×Landrace) of average body weight (BW), backfat thickness, and parity were assigned to one of six treatments with 10 or 11 sows per treatment in a completely randomized design. Experimental diets with different CP levels were as follows: i) CP11, corn–soybean-based diet containing 11% CP; ii) CP12, corn–soybean-based diet containing 12% CP; iii) CP13, corn–soybean-based diet containing 13% CP; iv) CP14, corn–soybean-based diet containing 14% CP; v) CP15, corn–soybean-based diet containing 15% CP; and vi) CP16: corn–soybean-based diet containing 16% CP. Results There was no significant difference in the performance of sow or piglet growth when sows were fed different dietary protein levels. Milk fat (linear, p = 0.05) and total solids (linear, p = 0.04) decreased as dietary CP levels increased. Increasing dietary CP levels in the gestation diet caused a significant increase in creatinine at days 35 and 110 of gestation (linear, p = 0.01; linear, p = 0.01). The total protein in sows also increased as dietary CP levels increased during the gestation period and 24 hours postpartum (linear, p = 0.01; linear, p = 0.01). During the whole experimental period, an increase in urea in sows was observed when sows were fed increasing levels of dietary CP (linear, p = 0.01), and increasing blood urea nitrogen (BUN) concentrations were observed as well. In the blood parameters of piglets, there were linear improvements in creatinine (linear, p = 0.01), total protein (linear, p = 0.01), urea (linear, p = 0.01), and BUN (linear, p = 0.01) with increasing levels of dietary CP as measured 24 hours postpartum. At two measurement points (days 35 and 110) of gestation, the odor gas concentration, including amine, ammonia, and hydrogen sulfide, increased linearly when sows fed diets with increasing levels of dietary CP (linear, p = 0.01). Moreover, as dietary CP levels increased to 16%, the odor gas concentration was increased with a quadratic response (quadratic, p = 0.01). Conclusion Reducing dietary CP levels from 16% to 11% in a gestating diet did not exert detrimental effects on sow body condition or piglet performance. Moreover, a low protein diet (11% CP) may improve dietary protein utilization and metabolism to reduce odor gas emissions in manure and urine in gestating sows.
Evaluation of Wood Species Identification Using CNN-Based Networks at Different Magnification Levels

—Wood species identification (WoodID) is a crucial task in many industries, including forestry, construction, and furniture manufacturing. However, this process currently requires highly trained individuals and is time-consuming. With the recent advances in machine learning and computer vision techniques, automatic WoodID using macro-images of cross-section wood has gained attention. Nevertheless, existing works have been evaluated on ad-hoc datasets with pre-fixed magnification levels. To address this issue, this paper proposes an evaluation of deep learning-based methods for WoodID on multiple datasets with varying magnification levels. Several popular Convolutional Neural Networks, including DenseNet, ResNet50, and MobileNet, were examined to identify the best network and magnification levels. The experiments were conducted on five datasets with different magnifications, including a self-collected dataset and four existing ones. The results demonstrate that the DenseNet121 network achieved superior accuracy and F1-Score on the 20X dataset. The findings of this study provide useful insights into the development of automatic WoodID systems for practical applications.
Administration of ketoprofen in postpartum sows to control the incidence of post-parturient disorders and improve piglet survival rate

Objective Inflammation and pain management in postpartum hyperprolific sows is currently an important animal welfare issue in the swine industry. The present study investigates effects of ketoprofen treatment on the incidence of post-parturient disorders, feed intake, colostrum yield, piglet colostrum intake, colostrum immunoglobulin G (IgG) and piglet mortality rate during the first 3 days of postnatal life. Methods In total, 61 Danish Landrace×Yorkshire crossbred sows and their offspring (n = 833) were included in the experiment. The sows were randomly distributed into two groups: i) control (n = 31), sows were treated with tolfenamic acid 2 mg per kg for 2 days postpartum; ii) ketoprofen (n = 30), sows were treated with ketoprofen 3 mg per kg for 2 days postpartum. The farrowing process of the sows was monitored for 24 h daily, and data associated with farrowing were collected. Piglet colostrum intake, sow colostrum yield and colostrum IgG were determined. Results During the first 3 days postpartum, the incidence of sows that had fever did not differ between control and ketoprofen groups (51.6% and 56.7%, respectively, p = 0.692). Piglet colostrum intake did not differ between control and ketoprofen groups (p = 0.736). However, the proportions of piglets that had inadequate colostrum intake were 71.3%, 22.6%, and 5.4% in those with birth weights of <1.0 kg, 1.0 to 1.29 kg, and ≥1.30 kg, respectively (p<0.001). The piglet mortality rate did not differ between control and ketoprofen groups (p = 0.808). Conclusion Administration of ketoprofen in postpartum sows for 2 days can control the evidence of post-parturient disorders in sows as effectively as the use of tolfenamic acid. No deleterious effect of ketoprofen was detected on sow colostrum yield, piglet colostrum intake and piglet mortality. Therefore, ketoprofen can be recommended as an alternative anti-inflammatory drug used in postpartum sows.
Seguridad cardiovascular de los nuevos fármacos para el tratamiento agudo y preventivo de la migraña: gepantes y ditanes

Introducción. La migraña es una patología neurológica prevalente caracterizada por ataques de cefalea incapacitantes. En las últimas décadas se han desarrollado nuevos fármacos específicos para el tratamiento agudo y preventivo de la migraña basados en su fisiopatología. Entre éstos se encuentran los antagonistas del péptido relacionado con el gen de la calcitonina (CGRP) (gepantes) y los agonistas selectivos del receptor serotoninérgico 5-HT1F (ditanes). El CGRP es un neuropéptido liberado por los terminales trigeminales que actúa como vasodilatador, provoca inflamación neurógena y, con ello, generación del dolor y sensibilización en la migraña. Posee, además, una potente acción vasodilatadora y participa en la regulación cardiovascular, razón por la cual se están llevando a cabo numerosos estudios que evalúan la seguridad vascular de actuar contra el CGRP. La alta selectividad de los ditanes para el receptor serotoninérgico 5-HT1F con una baja afinidad para otros receptores serotoninérgicos parece traducirse en un bajo o nulo efecto vasoconstrictor, que es mediado por la activación de los receptores 5-HT1B. Desarrollo. Nuestro objetivo es revisar la seguridad cardiovascular demostrada por estos nuevos fármacos para el tratamiento de la migraña analizando la evidencia publicada. Realizamos una búsqueda bibliográfica en la base de datos PubMed y una revisión de los ensayos clínicos publicados en clinicaltrial.gov. Incluimos revisiones bibliográficas, metaanálisis y ensayos clínicos en español e inglés. Analizamos los efectos adversos cardiovasculares informados. Conclusiones. Basándonos en los resultados hasta ahora publicados, podemos concluir que el perfil de seguridad cardiovascular de estos nuevos tratamientos es favorable. Para confirmar estos resultados son necesarios estudios de seguridad a más largo plazo.
Electromagnetic Band Gap Antenna with E-shaped Defected Ground Structure for Communication Systems

|A compact wideband miniaturized electromagnetic band gap (EBG) antenna has been proposed for communication systems with E-shaped defected ground structure (DGS). The proposed EBG antenna operates in the frequency range from 7.3 GHz to 9.4 GHz which includes the X band uplink frequency band (for sending modulated signals) from 7.9 to 8.4 GHz and the ITU-assigned downlink frequency band (for receiving signals) from 7.25 to 7.75 GHz. With EBG layer on the top layer, an E-shaped DGS structure has been introduced in the ground plane which results in the enhancement of measured impedance bandwidth from 300 MHz to 2100 MHz with good radiation characteristics.
Optical and motor changes associated with lighting and near vision tasks in electronic devices

Purpose: To assess optical and motor changes associated with near vision reading under different controlled lighting conditions performed with two different types of electronic screens. Methods: Twenty-four healthy subjects with a mean age of 22.9±2.3 years (18- 33) participated in this study. An iPad and an e-ink reader were chosen to present calibrated text, and each task lasted 5 minutes evaluating both ambient illuminance level and luminance of the screens. Results: Eye-tracker data revealed a higher number of saccadic eye movements under minimum luminance than under maximum luminance. The results showed statistically significant differences between the iPad (p=0.016) and the e-ink reader (p=0.002). The length of saccades was also higher for the minimum luminance level for both devices: 6.2±2.8 mm and 8.2±4.2 mm (e-ink max vs min), 6.8±2.9 mm and 7.6±3.6 mm (iPad max vs min), and blinking rate increased significantly for lower lighting conditions. Conclusions: Performing reading tasks on electronic devices is highly influenced by both the configuration of the screens and the ambient lighting, meanwhile, low differences in visual quality that are transient in healthy young people, were found.
Solar Energy Forecasting Based on Complex Valued Auto-encoder and Recurrent Neural Network

—Renewable energy is becoming a trusted power source. Energy forecasting is an important research field, which is used to provide information about the future power generation of renewable energy plants. Energy forecasting helps to safely manage the power grid by minimizing the operational cost of energy production. Recent advances in energy forecasting based on deep learning techniques have shown great success but the achieved results still too far from the target results. Ordinary deep learning models have been used for time series processing. In this paper, a complex-valued autoencoder was coupled with an LSTM neural network for solar energy forecasting. The complex-valued autoencoder was used to process the time series with the advantage of processing more complex data with more input arguments. The energy value was used as a real value and the weather condition was considered as the imaginary value. Taking into account the weather condition helps to better predict power generation. The proposed approach was evaluated on the Fingrid open data dataset. The mean absolute error (MAE), root-mean-square error (RMSE) and mean absolute percentage error (MAPE) was used to evaluate the performance of the proposed method. A comparison study was performed to prove the efficiency of the proposed approach. Reported results have shown the efficiency of the proposed approach.
Un algoritmo per l'addestramento di un analizzatore sintattico proiettivo utilizzando dipendenze non proiettive

Questa tesi sviluppa l'algoritmo per un nuovo oracolo per l'algoritmo Arc-Standard in modo da consentire l'utilizzo di dipendenze non proiettive nell'addestramento di un parser proiettivo, confrontandolo con altri metodi presenti in letteratura
The properties of RSA key generation process in software libraries

Diplomova praca porovnava rozne pristupy ku generovaniu RSA kľucov, hľadaniu prvocisel a testovaniu prvociselnosti v kryptografických knižniciach. Na zaklade znamych utokov na RSA kľuce su identifikovane vlastnosti zraniteľných kľucov. Algoritmy použivane v praxi su porovnane s odporucaniami a požiadavkami medzinarodných standardov. Distribucie prvocisel a RSA kľucov generovaných roznymi implementaciami su zatriedene podľa prislusných algoritmov. Aby bolo možne odhaliť algoritmy aj pri zdrojoch, ku ktorým nie je dostupný zdrojový kod programu, praca skuma ďalsie vlastnosti kľucov a procesu generovania kľucov.
Calculation of Consumer Baseline Load for Residential Sectors in the Context of Smart Grid

Abstract Demand response (DR) modifies the pattern from a straightforward to an interactive one, allowing for the investigation of consumer participation in the power market and progress. The most important technique for evaluating the status of DR program implementation among those discussed in the earlier literature is consumer baseline load (CBL). Without using the DR program, CBL depicts the consumer consumption pattern that might emerge. There hasn’t been much study done on CBL computation for residential consumers, and most of it ignores the influence of weather on these estimates. This study describes a novel method for computing CBL for residential consumers in the context of a smart grid. The findings show how weather affects CBL estimations and consumer desire to upgrade home insulation. The proposed method is examined in four test cases. The day-wise load data for the first test case has been collected from the NO1 region of Nord Pool and the remaining three test case data have been collected from the Connecticut and New Hampshire region of New England ISO.
MORAVA K - THEORY OF THE DOUBLE LOOP SPACES OF QUATERNIONIC STIEFEL MANIFOLDS

In this paper we get the Morava K - theory of the double loop spaces of quarternionic Stiefel manifolds for an odd prime p by computing the Atiyah - Hirzebruch spectral sequence. We also get the homology with Z=. p/ coefficients and analyze p torsion in the homology with Z coefficients.
Topologie de contact en dimension 3

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Voltammetric Determination of Trimethoprim Using a Glassy Carbon Electrode Modified with Printex(6L) Carbon and Gold Nanoparticles

This work proposes a simple, fast and low-cost voltammetric method for the determination of trimethoprim at low concentrations in an analytical and real matrix (river water sample, bovine serum and synthetic urine). For this, a glassy carbon electrode was modified with Printex(6L) carbon and gold nanoparticles in a chitosan film crosslinked with epichlorohydrin. After that, the electrochemical measurement system contained a solution of phosphate buffer at pH 4.0 with commands for the square wave voltammetry technique. The results achieved showed a limit of detection equal to 12.4 nmol L−1 and a linear concentration range from 0.20 to 6.0 μmol L−1. The sensor selectivity was tested in the presence of various electroactive molecules, and the results showed that the detection of TMP in the presence of possible interferents was not masked. In addition, the applicability of the AuNPs–Printex(6L)–CTS:EPH/GCE sensor was also verified in synthetic samples of urine, bovine serum and river water through standard addition and recovery tests. Finally, the results of this analytical proposal portray a simple, fast and efficient method for the detection of TMP in different matrices.
On swap rate dynamics: to freeze or not to freeze?

ABSTRACT We explore the implications of a common market and academic practice which is known as ‘freezing the drift’ when dealing with swap interest rate dynamics. In mathematical terms this can be better understood as imposing a low variance martingale (LVM) assumption. We look into the LVM assumption implications, both on the shape and dynamics for default-free yield curves. We show that the LVM assumption is equivalent to consider future yield curves are nothing but deterministic translations of the initial curve. For the particular case of the Nelson–Siegel yield curve calibration, we show the LVM assumption requires a deterministic parameter's evolution and, thus, imposes the need to constantly recalibrate the model. Finally, based upon European Central Bank historical data on evolution of the default-free Euro area yield curve, we illustrate periods in which the LVM may be applicable and others in which is not.
Times they are a-changin’: a response to Magli and Belmonte

Debates about archaeological interpretation are always healthy and welcome, and when, as here, arguments are not killed off, they inevitably become stronger. Importantly, the comments by Magli and Belmonte (2023) serve as a reminder that reconciling conflicting theoretical perspectives is never easy when times are changing. Grounded in positivism, their critique draws upon assumptions that become justificatory assertions. For better or worse, archaeological thinking has moved away from the processualism of the late 1960s, 1970s and early 1980s—a key turning point being Hodder's (1984) review of positivism in archaeological interpretation and his call for more contextualised approaches. The authors might usefully have consulted this and other works before setting out onto the choppy waters of post-processual archaeology. Many of their points simply escalate questions posed in my original text (Darvill 2022), and they often fail to distinguish between suggestions, arguments and more solid interpretative statements. Notwithstanding, they neatly arrange their response around three issues that are here addressed seriatim.
Classification with K-Nearest Neighbors Algorithm: Comparative Analysis between the Manual and Automatic Methods for K-Selection

—Machine learning and the algorithms it uses have been the subject of many and varied studies with the development of artificial intelligence in recent years. One of the popular and widely used classification algorithms is the nearest neighbors’ algorithm and in particular k nearest neighbors. This algorithm has three important steps: calculation of distances; selection of the number of neighbors; and the classification itself. The choice of the value for the k parameter determines the number of neighbors and is important and has a significant impact on the degree of efficiency of the created model. This article describes a study of the influence of the way the k parameter is chosen - manually or automatically. Data sets, used for the study, are selected to be as close as possible in their features to the data generated and used by small businesses - heterogeneous, unbalanced, with relatively small volumes and small training sets. From the obtained results, it can be concluded that the automatic determination of the value of k can give results close to the optimal ones. Deviations are observed in the accuracy rate and the behavior of well-known KNN modifications with increasing neighborhood size for some of the training data sets tested, but one cannot expect that the same model's parameter values (e.g. for k) will be optimally applicable on all data sets.
A Single-valued Pentagonal Neutrosophic Geometric Programming Approach to Optimize Decision Maker’s Satisfaction Level

—Achieving the desired level of satisfaction for a decision-maker in any decision-making scenario is considered a challenging endeavor because minor modifications in the process might lead to incorrect findings and inaccurate decisions. In order to maximize the decision-maker’s satisfaction, this paper proposes a Single-valued Neutrosophic Geometric Programming model based on pentagonal fuzzy numbers. The decision-maker is typically assumed to be certain of the parameters, but in reality, this is not the case, hence the parameters are presented as neutrosophic fuzzy values. The decision-maker, with this strategy, is able to achieve varying levels of satisfaction and dissatisfaction for each constraint and even complete satisfaction for certain constraints. Here the decision maker aims to achieve the maximum level of satisfaction while maintaining the level of hesitation and minimizing dissatisfaction in order to retain an optimum solution. Furthermore, transforming the objective function into a constraint adds one more layer to the N - dimensional multi-parametrizes and . The advantages of this multi-parametrized proposed method over the existing ones are proven using numerical examples.
Fusion Moves for

The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one possible way of achieving this by using graph cuts to combine pairs of suboptimal labelings or solutions. We call this combination process the fusion move. By employing recently developed graph-cut-based algorithms (so-called QPBO-graph cut), the fusion move can efficiently combine two proposal labelings in a theoretically sound way, which is in practice often globally optimal. We demonstrate that fusion moves generalize many previous graph-cut approaches, which allows them to be used as building blocks within a broader variety of optimization schemes than were considered before. In particular, we propose new optimization schemes for computer vision MRFs with applications to image restoration, stereo, and optical flow, among others. Within these schemes the fusion moves are used 1) for the parallelization of MRF optimization into several threads, 2) for fast MRF optimization by combining cheap-to-compute solutions, and 3) for the optimization of highly nonconvex continuous-labeled MRFs with 2D labels. Our final example is a nonvision MRF concerned with cartographic label placement, where fusion moves can be used to improve the performance of a standard inference method (loopy belief propagation).
TFF_aDCNN: A Pre-Trained Base Model for Intelligent Wideband Spectrum Sensing

In the beyond 5G (B5G)/6G era, to achieve ultra-dense and ultra-large-capacity intelligent connection of all things, an intelligent wideband spectrum sensing technology is particularly important. However, in an extremely wide frequency range, it is still a challenge to achieve high-precision and high-reconstruction-capability wideband spectrum sensing (WSS) under a very low SNR. We propose a Time-Frequency-Fused adjustable Deep Convolutional Neural Network (TFF_aDCNN). Meanwhile, a novel TFF_aDCNN-based sensing framework is also proposed. In this framework, we can obtain a pre-trained base model with a single distribution by training TFF_aDCNN. Then, for the sensing task in the actual environment, we use the base model for transfer learning, so that a newly trained sensing model can be obtained very quickly (i.e. fine-tuned model). In the TFF_aDCNN, we design a main network and an adjustable auxiliary network, where the former learns complex and abstract signal features, while the latter assists the main network in learning different data distribution patterns during the training process and regulates the focus direction of the main network during the perception process. Simulation results show that TFF_aDCNN can significantly reduce hardware cost and improve reconstruction accuracy and reconstruction capability, when compared with SOMP and SwSOMP-based WSS algorithms, single-dimensional deep learning spectrum sensing method, and deep learning-based WSS (DLWSS), especially at very low SNRs.