Miracle-dz/UniFAR-FTUs huggingface dataset:
Infant Feeding Practices That Substitute Exclusive Breastfeeding in a Semi-Rural Mexican Community: Types, Moments, and Associated Factors

International organizations recommend mothers practice exclusive breastfeeding (EBF) during the first six months of their infant’s life and introduce complementary feeding (CF) thereafter while continuing breastfeeding. However, the earlier introduction of liquids and foods is common worldwide and may have negative effects on breastfeeding practice, nutrition, and health. In this formative cross-sectional study, we interviewed 143 mothers from semi-rural communities in Tabasco, Mexico, whose infants were 4–6 months old. We explored (1) which feeding practices substituted EBF and (2) which factors were associated with each practice. During the first month of life, 42.7% of infants received formula milk (FM); this proportion increased to 74.5% by the sixth month. Adjusted Poisson regression analyses showed that giving FM was positively related to working away from home (PR 1.27; 95% CI 1.06, 1.54) and the perception that FM is an important food to accompany breast milk (PR 1.38; 95% CI 1.19, 1.70). Giving FM was negatively associated with not being sure the infant is full after breastfeeding (PR 0.75; 95% CI 0.61, 0.92). Regarding CF, less than half (47.5%) of infants had not received it by the fifth month. Factors positively associated with timely CF introduction were: the mother was told during prenatal care visits the optimal age to start CF is 6 months (PR 1.17, 95% CI 1.06, 1.29); she is convinced that giving only breast milk is best for her baby (PR 1.15, 95% CI 1.03, 1.29), and a higher infant weight-for-length (PR 1.04, 95% CI 1.00, 1.08) and length for age (PR 1.04, 95% CI 1.00, 1.09) z-scores at the study visit; conversely, it was negatively associated to the idea that if the infant is not full, she/he should receive formula milk or some other food (PR 0.87, 95% CI 0.78, 0.96). In these communities, EBF is lost to the use of FM and early CF. The factors associated with these inadequate feeding practices are related to returning to work, information received during prenatal visits, and the mother’s beliefs and thoughts. This work will guide the design of an intervention on infant feeding practices for these communities and other similar ones.
An assessment tool for the public opinion of the moral status of
 Artificial Intelligence

An Artificial intelligence’s rapidly progressing ability to plan actions and integrate and process information in a manner similar to humans, alongside an increasingly anthropomorphic conceptualization of AI’s underlying mechanisms has led experts in the fields of neuroscience, engineering, computer science, and philosophy to question whether or not AI has the ability to become conscious or sentient. Unclear criteria for defining consciousness and sentience as well as unclear criteria for determining moral status for humans, let alone non-human beings and entities makes it even more difficult to predict how AI with human-like abilities may interact with and function alongside humanity, or process how we will decide their moral and thus legal status in society. Considering that the public’s attitude and acceptance towards conscious AI will play a large role in deciding how AI are treated and whether or not they are respected as members of society, it is imperative to understand the public’s current opinions and perspective of the moral status of AI. This thesis aims to develop a robust assessment tool that can be used to neuroethically examine factors and themes crucial to the public’s opinion of the moral status of AI. The assessment tool was evaluated by conducting qualitative interviews with participants who offered insight into the clarity and effectiveness of the tool. A neuroethical analysis of the projected and emergent themes from the interviews influenced the refinement of the scenarios on the administered tool.
Parafoveal orthographic processing in bilingual reading

ABSTRACT Reading is a very complex task in which readers obtain information to promote reading from not only the fixated word located in the foveal area but also non-fixated words located in the parafoveal area. We aimed to investigate the second language (L2) parafoveal orthographic (letter identity and letter position) processing mechanism adopting the eye-tracking technique and boundary paradigm. We set up four previews for each target: (1) the identity preview (e.g. reporter → reporter), (2) the transposed-letter preview (e.g. repotrer → reporter), (3) the substituted-letter condition (e.g. repokcer → reporter), and (4) the unrelated preview (e.g. chemaful → reporter). There are three main findings. First, L2 readers could extract and utilize the parafoveal orthographic information shared by the preview and the target to affect the late L2 processing stage. Second, when there was only a small difference between the preview and the target, L2 readers did not notice the subtle difference in the parafovea. Third, the identity and position of an internal single letter have little effect on L2 reading compared with the similarity of the whole word in the parafoveal area. Future L2 reading frameworks should be developed to explain these new findings.
Predictive Multi Experiment Approach for the Determination of Conjugated Phenolic Compounds in Vegetal Matrices by Means of LC-MS/MS

Polyphenols (PCs) are a numerous class of bioactive molecules and are known for their antioxidant activity. In this work, the potential of the quadrupole/linear ion trap hybrid mass spectrometer (LIT-QqQ) was exploited to develop a semi-untargeted method for the identification of polyphenols in different food matrices: green coffee, Crocus sativus L. (saffron) and Humulus lupulus L. (hop). Several conjugate forms of flavonoids and hydroxycinnamic acid were detected using neutral loss (NL) as a survey scan coupled with dependent scans with enhanced product ion (EPI) based on information-dependent acquisition (IDA) criteria. The presented approach is focused on a specific class of molecules and provides comprehensive information on the different conjugation models that are related to specific base molecules, thus allowing a quick and effective identification of all possible combinations, such as mono-, di-, or tri-glycosylation or another type of conjugation such as quinic acid esters.
Thoughts about the development of tools for cognitive diagnosis of students’ writings in an e-learning environment

Students can be stimulated to become active learners using a tool for active writing. In our university we developed such a tool: POLARIS. Active writings of students about statistical concepts are valuable for the students and the teacher. In their writings students show their understanding of statistical topics. The problem then is how to interpret the writings of students in relation to their proficiency in statistics. Advances in cognitive psychology have extended our understanding of students’ learning and broadened the range of performances that can be used to acquire evidence about the developing abilities of the students. Furthermore advanced technology has made it possible to capture complex performances of students in assessment settings. In this paper the advances in both domains will be explored in order to propose a system to monitor and diagnose students’ on going learning.
Development of a simulation model as a decision support system for sugarcane supply

This research presents a discrete-event simulation model of cane supply as a decision support system for managers in a sugarcane mill. The research considers harvesting, transporting, and unloading cane at the mill yard, including the time windows, inherent uncertainty in the system, and queues of all operations. The model was implemented in C++, and the Qt Creator development environment was used to generate a graphical interface. We found that fifty percent of the time, the trailers are waiting; we also assessed the impact of mill downtime on the unloaded cane in the mill yard. Additional environments were also evaluated: rainy period, harvesting in remote places, and an alternative configuration at the unloading area that improves the process efficiency.
Na uptake at TiO_2 anatase surfaces under electric field control: A first-principles study

Na-ion batteries (NIBs) are promising devices for large-scale energy-storage facilities. Nanostructured TiO_2 is an efficient NIB negative electrode, showing good cycling performance and rate capability, but its activity depends on the crystalline facets exposed by anatase nanoparticles. Hence, we propose here a DFT+U study of Na^+ adsorption and insertion at (101), (100) and (001)-TiO_2 surfaces under the influence of external electric fields, which are simulated by adding a sawtooth-like electrostatic potential to the bare ionic potential. We find that field polarization affects Na^+ uptake as well as titania electronic features, promoting redox processes within Ti sublattice, as in battery charge/discharge cycling. Our results highlight the high-energy (001) surface to be the most active, for both directions of external fields, proving its activity to be exerted reversibly. Besides further insights, these outcomes pave the route for further exploration and design of electrode materials by simulation of battery in operando conditions. Graphical Abstract
Optimum contribution selection (OCS) analyses prompted successful conservation actions for Faroese Horse population

The Faroese horse, an endangered indigenous horse breed, is a part of the cultural and societal heritage of the Faroe Islands. Population history describes a severe bottleneck, prompting for quantification of the genetic diversity (level of inbreeding, probability of gene origin, effective population size) and assessment of sustainable conservation potential (Optimum Contribution Selection, OCS) of the Faroese horse population. The pedigree completeness (PCI) of the Faroese horse is adequate for a realistic estimation of the level of inbreeding (PCI5 = 0.96). In concordance with the known population history, the average inbreeding is exceptionally high; in the last cohort, it was equal to 26.8%. An estimate of the effective population size, based on individual increase in inbreeding and coancestry, accounting for the whole population history, was eight. OCS offers a tool to understand and control the increase in the average relationships in the population. Within a fixed number of matings, the repetitive use of stallions resulted in the lowest level of average relationships. Successful follow-up of mating schemes planned together with a holistic assessment of the suitability of an individual as a breeding candidate, will minimize the increase in inbreeding in future generations and maximize the possibility to increase the census size of the Faroese horse population.
Exploring invisibility and epistemic injustice in Long Covid—A citizen science qualitative analysis of patient stories from an online Covid community

In 2020, the long‐lasting effects of the Covid‐19 virus were not included in public messages of risks to public health. Long Covid emerged as a novel and enigmatic illness with a serious and life‐changing impact. Long Covid is poorly explained by objective medical tests, leading to widespread disbelief and stigma associated with the condition. The aim of this organic research is to explore the physical and epistemic challenges of living with Long Covid.
Quality and fatty acid profile of chicken sausage added canola oil as partial replacement for animal fat/ Qualidade e perfil de ácido graxo de linguiça de frango com substituição parcial de gordura animal por óleo de canola

The objective of this work was to evaluate the effects of the addition of canola oil to chicken sausage, mainly related to the fatty acid profile and sensory properties. The sausages were produced with the addition of canola oil-based emulsion to replace the saturated fat used to maintain cohesion of the ingredients stored at -8 °C, and the color, pH, and thiobarbituric acid reactive substance (TBARS) levels were determined on days 0, 7, 15, 30, and 45. A reduction in saturated fatty acid content and an increase in monounsaturated and polyunsaturated fatty acids were detected, and stored sausage samples had variable pH that remained within the range of 6.14 - 6.44 regardless of the concentration of substituted oil. Additionally, the TBARS levels were <0.79, indicating low levels of lipid peroxidation in sausage stored for 45 days. The substitution of animal fat with canola oil in sausage resulted in feasible proximal composition and sensory characteristics acceptable by non-trained panels. Moreover, the substitution resulted in nutritional improvements due to an increase in mono- and polyunsaturated fatty acids in the lipid fraction that specifically decreased lipid oxidation.
Building information modelling, integrated project delivery and all that

Purpose This paper aims to review the development of building information modelling (BIM) and integrated project delivery (IPD) in recent years and the process changes that BIM and IPD require. Design/methodology/approach A qualitative research methodology was applied which involved a comprehensive review of relevant literature leading to a better understanding of the history and development of BIM and IPD. A way forward is suggested for the future development of BIM and IPD. Findings The research revealed that the IPD approach is already ingrained within certain organisations and their supply chains. The issues of political will and business desire to change the existing procurement systems are examined. The need for fit with regional and national economic and cultural characteristics is a pre-requisite for successful change. Collaborative working, information exchange and trust only exist within the context of a trusted and reliable building information model that all can access, understand and manage. Originality/value This research pointed out that there is a need to overcome the institutional inertia that besets governments and their agencies and suggested that exemplar institutions and their projects are needed to lead the industry by integrating BIM into IPD through process change.
New FOM-Based Performance Evaluation of 600/650 V SiC and GaN Semiconductors for Next-Generation EV Drives

The drive inverter represents a central component of an electric vehicle (EV) drive train, being responsible for the DC/AC power conversion between the battery and the electrical machine. In this context, novel converter topologies adopting modern 600/650V wide bandgap (WBG) semiconductor devices will play a crucial role in improving the performance of next-generation drive inverters. In fact, WBG devices theoretically allow to achieve both higher inverter power density and higher conversion efficiency with respect to conventional silicon (Si) IGBT based solutions. Even though silicon carbide (SiC) devices are already well established in the automotive industry, high-voltage gallium nitride (GaN) devices are rapidly entering the market, promising higher theoretical performance but featuring a lower degree of maturity. As a consequence, it is currently not clear which semiconductor technology is most suited for future EV drive inverters. Therefore, this paper aims to address this gap providing a comparative performance evaluation of state-of-the-art SiC and GaN 600/650V active switches. In particular, a novel figure-of-merit (FOM) representing the minimum theoretical semiconductor losses under hard-switching operation is introduced. Remarkably, this FOM enables a fair and accurate performance comparison among semiconductor devices, allowing to clearly determine the best performing technology for a given set of application-specific conditions. The results of the comparative assessment show that currently available SiC and GaN active switch technologies can outperform each other depending on the semiconductor operating temperature and the converter switching frequency.
Turbulence models with adaptive meshing for industrial CFD

Computational fluid dynamics (CFD) and affordable computing power have advanced considerably in recent years, bringing full 3D simulation of complex high Reynolds number flows within reach of industry. However, providing accurate and trustworthy results in diverse flows with constraints on computational resources is still a considerable challenge. Owing to the complexity of commonly-encountered turbulent flows, robust turbulence models are required which do not have to be manually tuned to specific flow conditions to ensure their accuracy. In this regard, a highly effective approach is unstructured mesh adaptivity which automatically refines or coarsens the mesh locally in order to achieve a desired accuracy with minimum computational effort. However, the use of such adaptive meshes with turbulence models raises questions about the origins and interactions of various errors. This thesis describes the development, verification and validation of robust turbulence models suited to high Reynolds number single-phase turbulent flow using unstructured adaptive meshes. The main focus of this thesis is a new tensorial dynamic large eddy simulation (LES) model. The novel combination of the dynamic LES method with a tensorial definition of filter width is ideal for capturing the anisotropy and inhomogeneity of turbulence. This model is designed for use with unstructured mesh adaptivity, which enables the simulation of turbulent flow with high efficiency in terms of mesh resolution. Furthermore, the model is robust since both the resolution and the sub-filter-scale (SFS) stresses adapt to local flow conditions so that little a priori knowledge of the flow is required. Verification tests of the filtering method and validation of the new LES model in the 3D backward-facing step are presented. To provide context for the research, the contribution made by CFD simulations to the analysis of nuclear reactor safety and performance is discussed. The practicalities of performing simulations on high performance computing (HPC) facilities are also discussed. Background theory necessary to understand the research is presented, including a mathematical description of turbulent flow and the classes of CFD methods used to approximate it. A
Properties of Storm‐Time Magnetic Flux Transport

Magnetospheric convection, the transport of magnetic flux, along with mass, and energy, plays a vital role in magnetosphere‐ionosphere coupling and ring‐current energization. It is particularly intense during magnetic storms, a mode of circulation driven by prolonged and strong solar wind driving. A large body of work has quantified the evolution of flux circulation during nonstorm times, in particular during substorms and steady magnetospheric convection events (SMCs). However, transport during storms has not been duly characterized. Using magnetotail flux observations we explore storm‐time modes of convection and compare them to their nonstorm‐time counterparts. We find that storm‐time convection often encompasses loading‐unloading periods (akin to substorms) and SMCs (akin to those during nonstorm times). However, loading‐unloading events and SMCs are present during only 25% and 21% (respectively) of the (95) storms examined. We also investigate the solar wind conditions during these modes for storm versus nonstorm times. We find that these modes have characteristic profiles in AE, solar wind driving, and open flux content during storms that are similar to their nonstorm‐time counterparts, but larger in magnitude. Therefore, these convection modes occur during storm and nonstorm periods alike, and in particular they are not required for a storm to progress. Thus, what makes storms special, their intense ring current, is likely not attributable to the (un)steadiness of flux circulation, but to the prolonged and intense background flux circulation within which these modes are embedded, and hence to the ultimate strength of the solar wind driver.
Influence of Cereal Seed Orientation on External Friction Coefficients

The frictional properties of processed materials and their relationships with other parameters have to be determined to optimize production. In this study, the dimensions and mass of basic cereal seeds (wheat, rye, barley, oats, and triticale) and their external friction angles on a steel surface were determined for four seed orientations. Frictional properties were determined with two optoelectronic modules that measured the angle of static friction and the time required for a seed to travel across a given distance. The results were used to calculate the coefficient of sliding friction. Each seed was described with four shape factors and density variables. The relationships between the measured parameters were determined in an analysis of variance and a correlation analysis. The orientation of the hilum and crease relative to the steel surface significantly influenced the coefficient of static friction of the analyzed seeds. The lowest values of that coefficient were noted for seeds positioned with the crease up and the hilum down, and the highest values were found for seeds positioned with the crease down and the hilum up. The coefficient of sliding friction was determined in the range of 0.13 to 0.23, and it accounted for 39.5% and 64.3% of the value of the coefficient of static friction, respectively. Seed dimensions, seed mass, calculated shape factors, and seed density did not influence the coefficients of static and sliding friction. The coefficient of static friction was correlated mainly with seed thickness, and the coefficient of sliding friction was correlated with seed width.
Short Term Solar Irradiation Prediction Framework Based on EEMD-GA-LSTM Method

Accurate short term solar irradiation forecasting is necessary for smart grid stability and to manage bilateral contract negotiations between suppliers and customers. Traditional machine learning methods are unable to acquire and rectify nonlinear characteristics from solar dataset, which not only complicates model construction but also affect prediction accuracy. To address these issues, a deep learning based architecture with predictive analysis strategy is developed in this manuscript. In the first stage, the original solar irradiation sequences are divided into many intrinsic mode functions to generate a prospective feature set using a sophisticated signal decomposition technique. After that, an iteration method is used to generate a prospective range of frequency related to deep learning model. This method is created by linked algorithm using the GA and deep learning network. The findings by the proposed model employing sequences obtained by the preprocessing methodology considerable improve prediction accuracy as comparison to conventional models. In contrast, when confronted with a high resolution dataset derived from big data set, the chosen dataset may not only conduct a huge data reduction, but also enhances forecasting accuracy up to 22.74 percent over a variety of evaluation metrics. As a result, the proposed method might be used to predict short-term solar irradiation with greater accuracy using a solar dataset.
Electrical excitation of the local earth for resonant, wireless energy transfer

Here we demonstrate wireless energy transfer that exploits the conductivity and permittivity of soil to create a potential gradient on the surface around an earthed electrode, distributing electrical energy over the area. This generated surface potential can be amplified using a special standing-wave receiver for harnessing the distributed energy. We have experimentally mapped the surface potential around the electrode and plotted the received energy covering an area of 1200 m 2 . Key operating parameters are determined with a discussion on optimizing the system efficiency. This technique could address the challenge of distributing electrical energy to many low power devices over large outdoor areas without the use of wires.