Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of Plectranthus amboinicus in Feeding Fattening Pigs

Corresponding Author: Angel Roberto Sánchez Faculty of Agricultural Sciences, Universidad Técnica de Machala, Machala-Ecuador, Research Group on Food Production and Animal Health (GIPASA), Seedbed for Research in Animal Production (SIPA), Spain Email: arsanchez@utmachala.edu. ec Abstract: This research was developed at the Hacienda "Los Hermanos" belonging to the company Super Mac, located in Balao canton, Guayas province, coastal region of Ecuador, the purpose of this study was to evaluate the effect of the inclusion of ground dehydrated P. amboinicus in the feed of fattening pigs on their productive parameters and antibacsterial response. Four treatments were distributed, each one with 20 animals, regardless of sex for a total of 80 pigs, starting at 49 days of age and ending the experiment at 105 days, considering each pig as an experimental unit. A Completely Randomized Design (CRD) was used, with the following treatments: Treatment 1, control (T1), to which a commercial diet was administered, T2, T3, T4 to which 0.25, 0.50 and 0.75% of the dehydrated P. amboinicus was added to the commercial diet, respectively. For data analysis, the statistical program Statgraphics Centurion XV.I ® was used, applying a one factor Analysis (ANOVA) and to establish the differences between the means obtained, the procedure of the multiple comparison of Bonferroni was used with a 95% level of confidence, the variables evaluated were: Live weight gain and accumulated feed consumption in kg, feed conversion, mortality in percentage, total microbial flora and total coliforms in CFU. The results showed that, for the variables of productive parameters, there is no statistically significant difference, although there is a difference for the variables of total coliforms and total microbial flora when compared with T1, especially at the end of the experiment. As a conclusion there is an effect at the level of the antibacterial response, showing a positive correlation, the higher the percentage of dehydrated P. amboinicus included the greater efficacy in the control of CFUs. No mortality was found during this experiment.
The Effect of Cooperative Learning to Enhance Students’ Independence Learning in the 21 Century

The research aims to evaluate the effectiveness of cooperative learning through lesson study in enhancing the students’ self-learning in the twenty-first century. Classroom action research is the type of this study. The sample of this study was 24 students. The class is divided randomly into five small groups with 4 to 5 students. The cycle I showed that the average value of the motivation aspect was 3.17 or 79.25%; the discipline aspect was 3.37 or 84.25%; the initiative aspect was 3.21 or 80.25%, and the responsibility aspect was 3.25 or 81.25%. Cycle II found that the average value of the motivation aspect was 3.29 or 82.25%; the discipline aspect was 3.45 or 86.25%; the initiative aspect was 3.42 or 86.25%, and the responsibility aspect was 3.5 or 87.25%. In conclusion, it can be said that there was an improvement of students' autonomous learning by using Lesson Study.
Deep learning in resting-state fMRI*

Modeling the rich, dynamic spatiotemporal variations captured by human brain functional magnetic resonance imaging (fMRI) data is a complicated task. Analysis at the brain's regional and connection levels provides more straightforward biological interpretation for fMRI data and has been instrumental in characterizing the brain thus far. Here we hypothesize that spatiotemporal learning directly in the four-dimensional (4D) fMRI voxel-time space could result in enhanced discriminative brain representations compared to widely used, pre-engineered fMRI temporal transformations, and brain regional and connection-level fMRI features. Motivated by this, we extend our recently reported structural MRI (sMRI) deep learning (DL) pipeline to additionally capture temporal variations, training the proposed 4D DL model end-to-end on preprocessed fMRI data. Results validate that the complex non-linear functions of the used deep spatiotemporal approach generate discriminative encodings for the studied learning task, outperforming both standard machine learning (SML) and DL methods on the widely used fMRI voxel/region/connection features, except the relatively simplistic measure of central tendency - the temporal mean of the fMRI data. Additionally, we identify the fMRI features for which DL significantly outperformed SML methods for voxel-level fMRI features. Overall, our results support the efficiency and potential of DL models trainable at the voxel level fMRI data and highlight the importance of developing auxiliary tools to facilitate interpretation of such flexible models.
Gaussian Approximation for Bias Reduction in Q-Learning

Temporal-Difference off-policy algorithms are among the building blocks of reinforcement learning (RL). Within this family, Q-Learning is arguably the most famous one, which has been widely studied and extended. The update rule of Q-learning involves the use of the maximum operator to estimate the maximum expected value of the return. However, this estimate is positively biased, and may hinder the learning process, especially in stochastic environments and when function approximation is used. We introduce the Weighted Estimator as an effective solution to mitigate the negative effects of overestimation in Q-Learning. The Weighted Estimator estimates the maximum expected value as a weighted sum of the action values, with the weights being the probabilities that each action value is the maximum. In this work, we study the problem from the statistical perspective of estimating the maximum expected value of a set of random variables and provide bounds to the bias and the variance of the Weighted Estimator, showing its advantages over other estimators present in literature. Then, we derive algorithms to enable the use of the Weighted Estimator, in place of the Maximum Estimator, in online and batch RL, and we introduce a novel algorithm for deep RL. Finally, we empirically evaluate our algorithms in a large set of heterogeneous problems, encompassing discrete and continuous, low and high dimensional, deterministic and stochastic environments. Experimental results show the effectiveness of the Weighted Estimator in controlling the bias of the estimate, resulting in better performance than representative baselines and robust learning w.r. t. a large set of diverse environments.
Substrate integrated waveguide horn antenna with dielectric lens

A novel compact H‐plane horn antenna constructed is proposed for substrate integrated rectangular waveguide with ξr = 10.2. It has a dielectric arc lens for better directivity and a simple microstrip transition as feed. The horn, the lens, and the transition share the same substrate. The measured gain of this antenna is about 9 dB; the bandwidth, at 10 dB return loss, is over 12%, and the antenna size is 39.175 × 14 × 2 mm3. The frequency band is 25.5–28.5 GHz. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 168–178, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22086
Geometric Generalization of Self Tapping Screw Insertion Model

Bone screws are used in orthopaedic procedures to fix implants and stabilise fractures. These procedures require care, as improperly torquing the screws can lead to implant failure or tissue damage, potentially requiring revision surgery or causing further disability. It was proposed that automated torque-limit identification may allow clinical decision support to control the screw torque, and lead to improved patient outcomes. This work extends a previous model of the screw insertion process to model complex thread geometries used for bone screws; consideration was made for the variable material properties and behaviours of bone to allow further tuning in the future. The new model was simulated and compared with the original model. The model was found to be in rough agreement with the earlier model, but was distinct, and could model thread features that the earlier model could not, such as the fillets and curves on the bone screw profile. The new model shows promise in modelling the more advanced thread geometries of bone screws with higher accuracy.Clinical relevance: This work extends a self tapping screw model to support complex thread shapes, as common in bone screws, allowing more accurate modelling of the clinically relevant geometries.
UCATR: Based on CNN and Transformer Encoding and Cross-Attention Decoding for Lesion Segmentation of Acute Ischemic Stroke in Non-contrast Computed Tomography Images

The acute ischemic stroke (AIS) impacts extensively all over the world, the early diagnosis can provide valuable property information of disease. However, it’s difficult for our human eyes to distinguish the fine pathological changes. Here we introduce self-attention mechanisms and propose UCATR, an NCCT image segmentation network for AIS lesions. It uses the advantages of Transformer to effectively learn the global context features of the image, and is based on convolutional neural network (CNN) and Transformer as the encoder, adding Multi-Head Cross-Attention (MHCA) modules to the decoder to achieve high-precision spatial information recovery. This method is experimentally verified on the NCCT dataset of AIS provided by Chengdu Medical College in China to obtain that the Dice similarity coefficient of lesion segmentation is 73.58%, which is better than U-Net, Attention U-Net and TransUNet. Furthermore, we conduct ablation study on the MHCA module at three different positions in the decoder to prove its efficiency.
Winners and Losers: Some Thoughts about the Political Economy of Disaster

While it is obvious disasters are negative events causing injury and death, damage and destruction, macro-economic studies show little long-term economic effects from disaster. That is because disasters create both losers and winners and these balance out. Who loses and who wins is not random but a result of public policy decisions. The losers include individuals who are injured, lose their jobs, lose their home and families who lose a wage earner or a place of residence. The winners include individuals who earn extra money because they are involved in emergency response or restoration. They include wage earners and their families. They include some businesses, not others. They include communities which, because of substantial assistance, end up better off because of the disaster. Winners and losers are created by decisions about where to build a dam, who should receive what sort of assistance. This article is not based on new research but on an analysis of existing material. More research needs to be done on the economic effects on individuals and businesses and on communities and of the economic impact of policy decisions.
Procedures for Determining Maximum Emitter Discharge in Subsurface Drip Irrigation

One problem associated with subsurface drip irrigation (SDI) is the reduction in discharge resulting from soil-water back-pressure at the emitter outlets. An experimental setup was made to measure emitter discharge and pressure at the emitter outlet in different soils. Experiments were carried out with 2–24 L/h noncompensating and compensating emitters, operating at a constant lateral pressure of 10 m. Emitter discharge was reduced to a range of 2–10% for noncompensating models and to less than 1% for compensating models. Soil pressure ranged from 0.15–2.07 m. Laboratory conditions were simulated with HYDRUS-2D/3D. Experimental values of discharge and soil pressure showed good agreement with estimated values. Finally, maximum emitter discharge to limit the decrease of discharge was determined for an operating pressure of 10 m. For a 10% decrease, considering a constant radius of the spherical cavity in the soil, maximum emitter discharge was 2.35 L/h for loamy soil and 12.44 L/h for sandy soil for nonc.
Determination and control of resistance training intensity and volume in exercise science research and its application

In early studies, where the positive effects of resistance training and repeated muscular efforts were documented, the purpose of science to know the best way to define, control, and dose strength training has been one of the issues that have concentrated the greatest interest and effort. This issue is extremely important since the results that originate from the highest quality scientific works should make it possible to continue generating the body of knowledge that helps to improve the training methodology and, therefore, the participation in the practice of the professionals. For this to be accomplished, scientific studies must have, among other attributes, a precise method for determining and controlling the variables that define the proposed training stimulus to verify the relationship between it and the effects produced. However, if this does not happen, the researchers and training professionals themselves run the risk of making decisions about the configuration of the stimuli (manipulating the variables of the load) based on “false” scientific conclusions, or in the best of cases, uncertain.
An ICA Investigation into the Effect of Physiological Noise Correction on Dimensionality and Spatial Maps of Intrinsic Connectivity Networks

Physiological processes such as cardiac pulsations and respiration can induce signal modulations in functional magnetic resonance imaging (fMRI) time series, and confound inferences made about neural processing from analyses of the blood oxygenation level-dependent (BOLD) signals. Retrospective image space correction of physiological noise (RETROICOR) is a widely used approach to reduce physiological signals in data. Independent component analysis (ICA) is a valuable blind source separation method for analyzing brain networks, referred to as intrinsic connectivity networks (ICNs). Previously, we showed that temporal properties of the ICA-derived networks such as spectral power and functional network connectivity could be impacted by RETROICOR corrections. The goal of this study is to investigate the effect of retrospective correction of physiological artifacts on the ICA dimensionality (model order) and intensities of ICN spatial maps. To this aim, brain BOLD fMRI, heartbeat, and respiration were measured in 22 healthy subjects during resting state. ICA dimensionality was estimated using minimum description length (MDL) based on i.i. d. data samples and smoothness FWHM kernel, and entropy-rate based order selection by finite memory length model (ER-FM) and autoregressive model (ER-AR). Differences in spatial maps between the raw and denoised data were compared using the paired t-test and false discovery rate (FDR) thresholding was used to correct for multiple comparisons. Results showed that ICA dimensionality was greater in the raw data compared to the denoised data. Significant differences were found in the intensities of spatial maps for three ICNs: basal ganglia, precuneus, and frontal network. These preliminary results indicate that the retrospective physiological noise correction can induce change in the resting state spatial map intensity related to the within-network connectivity. More research is needed to understand this effect.
Brucellosis inCamels andHumans: SeroprevalenceandAssociated Risk Factors in Amibara District of Afar Region, Ethiopia

Animal Health Research Program, Holeta Agricultural Research Center, Ethiopian Institute of Agricultural Research, P.O. Box 31, Holeta, Ethiopia Department of Microbiology, Immunology and Veterinary Public Health, College of Veterinary Medicine and Agriculture, Addis Ababa University, P.O. Box 34, Bishoftu, Ethiopia Haramaya University, College of Veterinary Medicine, P.O. Box 138, Haramaya, Ethiopia
Estimation of Retinotopic Map of Awake Mouse Brain Based upon Retino-Cortical Response Model

We proposed a novel retino-cortical response model on which the fine retinotopic map of the primary visual cortex was estimated from the intrinsic optical signal (IOS) induced by visual stimulation in an awake mouse. Our method was developed to overcome practical restrictions of measurement time and disturbances such as eye movement and brain background activity instead of synchronous averaging. In our model, it was assumed that the response of the cortical region was given by integrating the product of the image projected on a spherical retina and the retino-cortical sensitive function. In addition, in order to estimate parameters of the sensitive function, Monte Carlo-based numerical integration and nonlinear least square algorithm were employed. By applying this method to the actual IOS data, we estimated a biologically plausible spatial distribution of the sensitivity function parameters and a retinotopic map. Similar to our pervious study, higher-order brain regions such as the secondary visual cortex were also visualized. These results suggested usefulness of our proposed method based on the novel retino-cortical response model.Clinical Relevance— The method for evaluating visual functions under restoration was proposed and its validity was examined in animal experiments.
Multi-Physical Tissue Modeling of a Human Urinary Bladder

A multi-physical model of a human urinary bladder is an essential element for the potential application of electrical impedance spectroscopy during transurethral resection surgery, where measurements are taken at different fill levels inside the bladder. This work derives a multi-physical bladder tissue model that incorporates the electrical impedance properties with dependence on mechanical deformation due to filling of the bladder. The volume and ratio of the intracellular to extracellular tissue fluid heavily influence the electrical impedance characteristics and thus provide the connection between the mechanical and electrical domains. Modeling the fluid within the tissue links both the physical and histological processes and enables useful inferences of the properties from empiric observations. This is demonstrated by taking impedance measurements at different fill volumes. The resulting model provides a tool to analyze impedance measurements during surgery at different stress levels. In addition, this model can be used to determine patient-specific tissue parameters.
A process view of maintenance and its stakeholders

Purpose – The purpose of this paper is to describe maintenance in a generic process model, in order to support an alignment of maintenance with other company internal processes aimed at fulfilling external stakeholder requirements.Design/methodology/approach – The proposed maintenance process model is based on existing theories and is illustrated by examples from a paper‐mill case study related to the maintenance of DC‐motors.Findings – The proposed model supports a holistic view of maintenance and the alignment of the maintenance process with other company internal processes, in order to fulfil external stakeholder requirements.Research limitations/implications – Further research could include an application of the proposed maintenance model to test its usefulness to identify stakeholders and also hazard diagnosis.Practical implications – The importance of vertical and horizontal alignment between the maintenance process and other processes in order to achieve effectiveness and efficiency is illustrated.
A Constrained Molecular Dynamics (CoMD) study of nuclear near-ground-state properties

Abstract. The Constrained Molecular Dynamics (CoMD) model is used to describe the properties of nuclear systems near the ground state. A procedure for global optimization of the initial configurations of the nuclei is developed. In addition, the neutron skins of various nuclear systems are calculated. Finally, the GDR and GMR spectra of medium-mass nuclear systems are studied. The effect of the model parameters to the spectra is explored. We conclude that an increased compressibility of K = 308 MeV results in increased GDR energy and decreased skin, while the total energy and the GMR energy remain almost unaltered.
Measurement and improvement of Quality-of-Experience for online video streaming services

HTTP based online video streaming services have been consis te tly dominating the online traffic for the past few years. Measuring and impro ving the performance of these services is an important challenge. Traditional Qual ity-of-Service (QoS) metrics such as packet loss, jitter and delay which were used for netw orked services are not easily understood by the users. Instead, Quality-of-Experience ( QoE) metrics which capture the overall satisfaction are more suitable for measuring the qu ality as perceived by the users. However, these QoE metrics have not yet been standardized an d their measurement and improvement poses unique challenges. In this work we first pr esent a comprehensive survey of the different set of QoE metrics and the measuremen t thodologies suitable for HTTP based online video streaming services. We then present our active QoE measurement tool Pytomothat measures the QoE of YouTube videos. A case study on the measurement of QoE of Yo uTube videos when
Evaluation of Mesh and Sensor Resolution for Finite Element Modeling of Non-Invasive Fetal ECG Signals

Non-invasive fetal electrocardiography (NI-FECG) is an emerging tool with novel diagnostic potential for monitoring fetal wellbeing using electrical signals acquired from the maternal abdomen. However, variations in the geometric structure and conductivity of maternal-fetal tissues have been shown to affect the reliability of NI-FECG signals. Previous studies have utilized detailed finite element models to simulate these impacts, however this approach is computationally expensive. In this study, we investigate a range of mesh and sensor resolutions to determine an optimal trade-off between computational cost and modeling accuracy for simulating NI-FECG signals. Our results demonstrate that an optimal refinement of mesh resolution provides comparable accuracy to a detailed reference solution while requiring approximately 12 times less computation time and one-third of the memory usage. Furthermore, positioning simulated sensors at a 20 mm grid spacing provides a sufficient representation of abdominal surface potentials. These findings represent default parameters to be used in future simulations of NI-FECG signals. Code for the model utilized in this work is available under an open-source GPL license as part of the fecgsyn toolbox.Clinical Relevance— Simulating NI-FECG signals provides the opportunity to study the effects of sensor placement and maternal-fetal anatomic variations in a controlled setting. This work has relevance in determining default parameters for efficiently performing these simulations.
Does Copyright Affect Reuse? Evidence from the Google Books Digitization Project

While digitization projects like Google Books have dramatically increased access to information, this study examines how the ability to reuse digital information and diffuse knowledge to a wider audience critically depends on features of copyright law. I use the digitization of both copyrighted and non-copyrighted issues of Baseball Digest by the Google Books Digitization Project to measure the impact of copyright on a prominent venue for reuse: Wikipedia. A specific feature of the 1909 Copyright Act, copyright renewal requirements ensure that material published before 1964 is out of copyright, which allows for causal estimation of the impact of copyright on Wikipedia across this sharp cutoff. I find that digitization by Google Books increased the reuse of Baseball Digest on Wikipedia, but copyright hurt reuse by reducing citations to copyrighted issues by over 135%. Copyright also had real impacts on readership – affected pages had, on average, 20% lower gains in internet traffic than pages unaffected by copyright. Finally, the impact of copyright on digitized material was highly uneven. Copyright mostly affected the reuse of images rather than text, and prevented reuse of information about less-popular players as compared to more popular ones.