Miracle-dz/UniFAR-FTUs huggingface dataset:
Individualistic impact of unit operations of production, at household level, on some antinutritional factors in selected cowpea‐based food products

Abstract The individualistic effect of unit operations of production, at household level, on some antinutritional factors in selected cowpea‐based food products (moin‐moin, akara, and gbegiri) was investigated. Four cowpea types (IT93K‐452‐1, IT95K‐499s‐35, IT97K‐568‐18, and market sample) were used for the study, whereas the three traditional food products were produced from each of the cowpea types, respectively. The results revealed that every unit operation involved in the production of moin‐moin, akara or gbegiri contributed to the overall reduction of trypsin inhibitor activity (TIA), phytic acid (PA), and tannin; though at varying degrees. In the production of moin‐moin, the major contributions to the overall reduction in TIA were from steaming (64.2–72.0%), second‐stage soaking (9.7–11.9%), and dehulling (9.4–10.2%). The contributions to the overall reduction in PA were from dehulling (34.0–40.4%), preliminary soaking (15.4–21.0%), and steaming (7.8–14.0%), whereas that of tannin were from dehulling (39.7–47.6%), steaming (19.6–24.7%), and preliminary soaking (9.8–15.9%). For akara production, the major contributions to TIA reduction were from deep frying (64.2–72.0%), second‐stage soaking (9.7–11.9%), and dehulling (9.4–10.2%). The PA reduction was from dehulling (34.0–40.4%), preliminary soaking (15.4–21.0%), and deep frying (9.6–15.9%), whereas that of tannin reduction was from dehulling (39.7–47.6%), deep frying (20.7–25.3%), and preliminary soaking (9.8–15.9%). In the production of gbegiri, the overall reduction in TIA was contributed from pressure cooking (79.0–84.8%), preliminary soaking (5.8–11.3%), and dehulling (9.4–10.2%). The reduction in PA was contributed by dehulling (34.0–40.4%), pressure cooking (24.7–35.0%), and preliminary soaking (15.4–21.0%), whereas the overall reduction in tannin content was similarly contributed by dehulling (39.7–47.6%), pressure cooking (29.8–34.4%), and preliminary soaking (9.8–15.9%).
Study on the optimal order for the auto-regressive time-frequency analysis of heart rate variability

Time-frequency analysis of heart rate variability (HRV) makes it easier to evaluate how the balance between the sympathetic and parasympathetic influences on heart rhythm varies with time. The auto-regressive model can be used to calculate the power spectrum density of HRV and to create an auto-regressive spectrogram. This work presents these techniques and describes a series of tests performed with the goal of determining the AR order that is more adequate for the calculation of the AR spectrogram. As a result, ranges of optimal orders for different interpolation rates of the HRV signal are presented.
Bayesian Analysis of the Conditional Correlation Between Stock Index Returns with Multivariate SV Models

In the paper we compare the modelling ability of discrete-time multivariate Stochastic Volatility models to describe the conditional correlations between stock index returns. We consider four trivariate SV models, which differ in the structure of the conditional covariance matrix. Specifications with zero, constant and time-varying conditional correlations are taken into account. As an example we study trivariate volatility models for the daily log returns on the WIG, SP500, and FTSE100 indexes. In order to formally compare the relative explanatory power of SV specifications we use the Bayesian principles of comparing statistic models. Our results are based on the Bayes factors and implemented through Markov Chain Monte Carlo techniques. The results indicate that the most adequate specifications are those that allow for time-varying conditional correlations and that have as many latent processes as there are conditional variances and covariances. The empirical results clearly show that the data strongly reject the assumption of constant conditional correlations.
A starquake model for the Vela pulsar

The measured values of the glitch healing parameter, Q, of the Vela pulsar are found to be inconsistent with the starquake mechanism for glitch generation in various neutron star (NS) models, based upon the parametrized equations of state (EOSs) of dense nuclear matter. Such models correspond to an unrealistic mass range ≤0.5 M ⊙ for the pulsar, if the observational constraints of the fractional moment of inertia of the core component (I core /I total ≤ 0.2) which is called the glitch healing parameter, Q, according to the starquake model, are imposed on these models. However, we show that these observational constraints yield a realistic mass range for NS models, corresponding to a core given by the stiffest equation of state, dP/dE = 1 (in geometrized units), and the envelope is characterized by the well-known EOS of an adiabatic polytrope (d In P/d In p = Γ 1 ), if the continuity of the adiabatic speed of sound (υ = dP/dE), together with the pressure (P), the energy density (E) and the two metric parameters (v and λ), is assured at the core-envelope boundary of the models and this boundary is worked out on the basis of the 'compatibility criterion' for hydrostatic equilibrium. The models yield a stable sequence of NS masses in the range 1.758 ≤ M ≤ 2.2 M ⊙ , corresponding to the glitch healing parameter range 0 < Q < 0.197, for a choice of the 'transition density' E b = 1.342 × 10 15 g cm -3 at the core-envelope boundary. The maximum stable value of 2.2 M ⊙ in this sequence, in fact, corresponds to the lowest possible upper bound on NS masses calculated in the literature, on the basis of modem EOSs for NS matter. The models yield the surface redshift z R ≃ 0.6913 and mass M ≃ 2.153 M ⊙ for the 'central' weighted mean value, Q = 0.12 ± 0.07, of the glitch healing parameter of the Vela pulsar. This value of mass can increase slightly up to M ≃ 2.196 M ⊙ , whereas the surface redshift can increase up to the value z R ≃ 0.7568 [which represents an ultracompact object (zR ≥ 0.73)], if the observational constraint of the upper weighted mean value of Q ≃ 0.19 is imposed on these models. However, for the lower weighted mean value of Q ≃ 0.05, the mass and surface redshift can decrease to the values of z R ≃ 0.6066 and M ≃ 2.052 M ⊙ respectively. These results set the lower bound on the energy of gravitationally redshifted radiation in the rather narrow range of 0.291-0.302 MeV. The observation of the lower bound on the energy of a γ-ray pulse at about 0.30 MeV from the Vela pulsar in 1984 is in excellent agreement with this result, provided that this energy can be interpreted as the energy of gravitationally redshifted electron-positron annihilation radiation from the surface of the star.
High Resolution Mid-Infrared Imaging of Ultraluminous Infrared Galaxies

Observations of ultraluminous infrared galaxies (ULIRGs) with an achieved resolution approaching the diffraction limit in the mid-infrared from 8 to 25 μm using the Keck Telescopes are reported. We find extremely compact structures, with spatial scales of less than 0. ″3 (diameter) in six of the seven ULIRGs observed. These compact sources emit between 30% and 100% of the mid-infrared energy from these galaxies. We have utilized the compact mid-infrared structures as a diagnostic of whether an AGN or a compact (100–300 pc) starburst is the primary power source in these ULIRGs. In Markarian 231, the upper limit on the diameter of the 12.5 μm source, 0. ″13, shows that the size of the infrared source must increase with increasing wavelength, consistent with AGN models. In IRAS 05189-2524 and IRAS 08572+3915 there is strong evidence that the source size increases with increasing wavelength. This suggests heating by a central source rather than an extended luminosity source, consistent with the optical classification as an AGN. The compact mid-infrared sources seen in the other galaxies cannot be used to distinguish the ultimate luminosity source. If these ULIRGs are powered by compact starbursts, the star formation rates seen in the central few hundred parsecs far exceed the global rates seen in nearby starburst galaxies, and approach the surface brightness of individual clusters in nearby starburst galaxies.
Primary and Secondary Rewards Differentially Modulate Neural Activity Dynamics during Working Memory

Background Cognitive control and working memory processes have been found to be influenced by changes in motivational state. Nevertheless, the impact of different motivational variables on behavior and brain activity remains unclear. Methodology/Principal Findings The current study examined the impact of incentive category by varying on a within-subjects basis whether performance during a working memory task was reinforced with either secondary (monetary) or primary (liquid) rewards. The temporal dynamics of motivation-cognition interactions were investigated by employing an experimental design that enabled isolation of sustained and transient effects. Performance was dramatically and equivalently enhanced in each incentive condition, whereas neural activity dynamics differed between incentive categories. The monetary reward condition was associated with a tonic activation increase in primarily right-lateralized cognitive control regions including anterior prefrontal cortex (PFC), dorsolateral PFC, and parietal cortex. In the liquid condition, the identical regions instead showed a shift in transient activation from a reactive control pattern (primary probe-based activation) during no-incentive trials to proactive control (primary cue-based activation) during rewarded trials. Additionally, liquid-specific tonic activation increases were found in subcortical regions (amygdala, dorsal striatum, nucleus accumbens), indicating an anatomical double dissociation in the locus of sustained activation. Conclusions/Significance These different activation patterns suggest that primary and secondary rewards may produce similar behavioral changes through distinct neural mechanisms of reinforcement. Further, our results provide new evidence for the flexibility of cognitive control, in terms of the temporal dynamics of activation.
Reversing the Academic Trend for Rural Students: The Case of Michelle Opbroek.

This case study explores the interactions between a teacher, her students, and a culturally based math curriculum in a fifth and sixth grade classroom in rural Alaska. The case attempts to identify and illuminate factors that created a rich learning environment while implementing Star Navigation: Explorations into Angles and Measurement, a module from the series, Math in a Cultural Context (MCC). This case describes how the teacher facilitated the embedded Yup’ik cultural knowledge into lively, mathematical communication and learning made relevant to a non-Yup’ik group of students. Students’ preand post-test results showed strong gain scores as well as high absolute post-test scores, placing this class in the small category where a rural treatment group outperformed all urban treatment and control groups. Thus, this compelling case provides an example of a classroom and curricular learning environment that reverses national trends for rural students in general and shows potential for Alaska Native students in particular. Further, it provides examples of factors that other teachers, administrators, and teacher educators can employ in their own teaching and classes to create more effective math classrooms.
Semi-quantitative analysis of transcript accumulation in response to drought stress by Lepidium latifolium seedlings

Cross-amplification of five Arabidopsis abiotic stress-responsive genes (AtPAP, ZFAN, Vn, LC4 and SNS) in Lepidium has been documented in plants raised out of seeds pre-treated with potassium nitrate (KNO3) for assessment of enhanced drought stress tolerance. cDNA was synthesized from Lepidium plants pre-treated with KNO3 (0.1% and 0.3%) and exposed to drought conditions (5% and 15% PEG) at seedling stage for 30 d. Transcript accumulation of all the five genes were found suppressed in set of seedlings, which were pre-treated with 0.1% KNO3 and were exposed to 15% PEG for 30 d. The present study establishes that different pre-treatments may further enhance the survivability of Lepidium plants under conditions of drought stress to different degrees.
A PRECEDE-PROCEED based educational intervention in quality of life of women-headed households in Iran

Background: Women-headed households are more exposed to social damages than other women. Such condition remarkably influences the women’s health-related life quality. The present study is aimed to investigate the effect of an educational intervention in quality of life of women-headed households under protection of Tehran Welfare Organization, in 2015. Methods: In this quasi-experimental study with control group, 180 women-headed households participated. Sampling method was random allocation. Data collection tools were Life Quality standard questionnaire (WHOQOL-BREF) and a researcher-made questionnaire about structures of ecological and educational diagnosis phase of PRECEDE-PROCEED model. Validity and reliability of the questionnaire approved in a primary study. Based on the results obtained from the primary study, the intervention was performed in the case group only. Participants were followed one and three months after intervention. Data were analyzed through SPSS v. 15 software using descriptive and analytical tests. Results: Before intervention no significant difference was observed among the mean scores of life quality, behavioral factors, and knowledge, enabling, and reinforcing factors in the two groups. But, one month and three months after intervention a significant difference was observed between the mean scores of these variables (in five instances p<0.001). Conclusion: Intervention through the PRECEDE-PROCEED model improved the women-headed households’ quality of life. The innovation of this study is using such intervention on quality of life in women-headed households for the first time.
Implications of selection for meat and wool traits on maternal performance in Merinos.

The impact of selection for meat and wool on maternal performance was examined in a large stud Merino flock in southwest Western Australia. Australian Sheep Breeding Values for all important production traits and full lambing records were available for the six years studied. Analyses were completed to determine if any of the production traits were correlated with ewe fecundity, lamb birthweight and lamb survival. The effects of Birth Type, Year, Dam Age and Sire were also included in the analyses. Ewe fecundity was positively correlated with ewe body weight and muscling. Lamb birthweight was negatively correlated with ewe muscling and fleece weight. In single-born lambs, survival decreased as ewe fleece weight increased, independently of lamb birthweight. This correlation was not evident in twin-born lambs. The correlations identified in this paper are important for the Australian sheep industry and breeders should consider the impact of the selection for wool and meat traits on maternal performance when designing their breeding programs.
Detecting Semantic Uncertainty by Learning Hedge Cues in Sentences Using an HMM

Detecting speculative assertions is essential to distinguish semantically uncertain information from the factual ones in text. This is critical to the trustworthiness of many intelligent systems that are based on information retrieval and natural language processing techniques, such as question answering or information extraction. We empirically explore three fundamental issues of uncertainty detection: (1) the predictive ability of different learning methods on this task; (2) whether using unlabeled data can lead to a more accurate model; and (3) whether closed-domain training or crossdomain training is better. For these purposes, we adopt two statistical learning approaches to this problem: the commonly used bag-of-words model based on Naive Bayes, and the sequence labeling approach using a Hidden Markov Model (HMM). We empirically compare between our two approaches as well as externally compare with prior results on the CoNLL-2010 Shared Task 1. Overall, our results are promising: (1) onWikipedia and biomedical datasets, the HMM model improves over Naive Bayes up to 17.4% and 29.0%, respectively, in terms of absolute F score; (2) compared to CoNLL-2010 systems, our best HMMmodel achieves 62.9% F score with MLE parameter estimation and 64.0% with EM parameter estimation on Wikipedia dataset, both outperforming the best result (60.2%) of the CoNLL-2010 systems, but our results on the biomedical dataset are less impressive; (3) when the expression ability of a model (e.g., Naive Bayes) is not strong enough, cross-domain training is helpful, and when a model is powerful (e.g., HMM), cross-domain training may produce biased parameters; and (4) under Maximum Likelihood Estimation, combining the unlabeled examples with the labeled helps.
On Almost Sure Stabilization of Continuous-Time Markov Jump Linear Systems

In this work we derive an easily testable sufficient condition for assessing almost sure (AS) stability of a continuous-time Markov jump linear system (MJLS) with a finite state Markov form process. Such a condition is used to design a feedback stabilization strategy under the hypothesis that at least one mode is controllable. The proposed condition relies on some bounds on the 2-norm of the transition matrix over the time interval to the first jump. Such bounds are computed from the eigenvalues of the solutions of a set of associated Lyapunov equations, one for each mode. An algorithm is also given for designing the stabilizing feedback, based on a formulation of the sufficient condition in terms of an equivalent LMI problem
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization

We propose a new method for fine-grained object recognition that employs part-level annotations and deep convolutional neural networks (CNNs) in a unified framework. Although both schemes have been widely used to boost recognition performance, due to the difficulty in acquiring detailed part annotations, strongly supervised fine-grained datasets are usually too small to keep pace with the rapid evolution of CNN architectures. In this paper, we solve this problem by exploiting inexhaustible web data. The proposed method improves classification accuracy in two ways: more discriminative CNN feature representations are generated using a training set augmented by collecting a large number of part patches from weakly supervised web images, and more robust object classifiers are learned using a multi-instance learning algorithm jointly on the strong and weak datasets. Despite its simplicity, the proposed method delivers a remarkable performance improvement on the CUB200-2011 dataset compared to baseline part-based R-CNN methods, and achieves the highest accuracy on this dataset even in the absence of test image annotations.
Seed Priming with Polyethylene Glycol Induces Physiological Changes in Sorghum (Sorghum bicolor L. Moench) Seedlings under Suboptimal Soil Moisture Environments

Osmopriming with PEG has potential to improve seed germination, seedling emergence, and establishment, especially under stress conditions. This research investigated germination performance, seedling establishment, and effects of osmopriming with PEG on physiology in sorghum seedlings and their association with post-priming stress tolerance under various soil moisture stress conditions. Results showed that seed priming increased the environmental range suitable for sorghum germination and has potential to provide more uniform and synchronous emergence. Physiologically, seed priming strengthened the antioxidant activities of APX, CAT, POD, and SOD, as well as compatible solutes including free amino acid, reducing sugar, proline, soluble sugar, and soluble protein contents. As a result, seed priming reduced lipid peroxidation and stabilized the cell membrane, resulting in increased stress tolerance under drought or excessive soil moisture environments. Overall, results suggested that seed priming with PEG was effective in improving seed germination and seedling establishment of sorghum under adverse soil moisture conditions. Osmopriming effectively strengthened the antioxidant system and increased osmotic adjustment, likely resulting in increased stress tolerance.
Road Traffic Accident Victims’ Experiences of Return to Normal Life: A Qualitative Study

Background Road traffic accident (RTA) victims also suffer from different types of injuries and disabilities, which can affect their quality of life. They usually face with various physical, mental, and social problems. Most traffic accident victims had difficulty to return to normal life. Objectives This study aimed to understand the experiences of return to normal life in RTA victims. Patients and Methods This qualitative study with content analysis approach was conducted on 18 Iranian patients with disability in the upper or lower limbs caused by traffic accidents, who had passed a time between 3 months till 2 years. A purposeful sampling method was applied until reaching data saturation. Data were collected using semi-structured interviews. Afterwards, the gathered data were analyzed through conventional content analysis. Results By analyzing 498 primary codes, four main categories, including supportive needs, adaptation to the new situation, seeking information, and transition from functional limitation, were extracted from traffic accident victims’ experiences of reintegration to normal life. Conclusions The results of this study may help policy-makers to take steps toward health promotion and recovery of RTA victims. Considering the results of this study, it is a need for further research to investigate RTAs victims’ needs for reintegration to home and community. Access to training and supportive facilities like strong therapeutic, nursing and social support, and the possibility to participate in self-care activities is essential for reintegration to community in RTA victims.
National Association of State Boards of Education

Federal regulations under No Child Left Behind (NCLB) restricted the authority of state boards of education (SBEs) in many areas of state policy, making it diffi cult to align a state’s education policies. The Every Student Succeeds Act (ESSA) releases states from many of NCLB’s restrictive measures and gives states autonomy to establish a coherent system of policies focused on student learning. Yet in the rush to focus on ESSA’s nuts and bolts, states may lose sight of foundational issues that will guide long-term development of an effective, effi cient standards-based system. New Windows of Opportunity Thanks to ESSA