Miracle-dz/UniFAR-FTUs huggingface dataset:
Students' Understanding of the Particulate Nature of Matter

The particulate nature of matter is identified in science education standards as one of the fundamental concepts that students should understand at the middle school level. However, science education research in indicates that secondary school students have difficulties understanding the structure of matter. The purpose of the study is to describe how engaging in an extended project-based unit developed urban middle school students' understanding of the particulate nature of matter. Multiple sources of data were collected, including pre- and posttests, interviews, students' drawings, and video recordings of classroom activities. One teacher and her five classes were chosen for an indepth study. Analyses of data show that after experiencing a series of learning activities the majority of students acquired substantial content knowledge. Additionally, the finding indicates that students' understanding of the particulate nature of matter improved over time and that they retained and even reinforced their understanding after applying the concept. Discussions of the design features of curriculum and the teacher's use of multiple representations might provide insights into the effectiveness of learning activities in the unit.
A synoptic-dynamic model of subseasonal atmospheric variability

Abstract A global synoptic–dynamic model (GSDM) of subseasonal variability is proposed to provide a framework for real-time weather–climate monitoring and to assist with the preparation of medium-range (e.g., week 1–3) predictions. The GSDM is used with a regional focus over North America during northern winter. A case study introduces the time scales of the GSDM and illustrates two circulation transitions related to eastward-moving wave energy signals and their connection to remote tropical forcing. Global and zonal atmospheric angular momentum (AAM) is used to help define the synoptic evolution of the GSDM components and to link regional synoptic variations with physical processes like the global mountain and frictional torque. The core of the GSDM consists of four stages based on the Madden–Julian oscillation (MJO) recurrence time. Additionally, extratropical behaviors including teleconnection patterns, baroclinic life cycles, and ∼monthly oscillations provide intermediate and fast time scales that are.
Group I Metabotropic Glutamate Receptors Modulate Motility and Enteric Neural Activity in the Mouse Colon

Glutamate is the major excitatory neurotransmitter in the central nervous system, and there is evidence that Group-I metabotropic glutamate receptors (mGlu1 and mGlu5) have established roles in excitatory neurotransmission and synaptic plasticity. While glutamate is abundantly present in the gut, it plays a smaller role in neurotransmission in the enteric nervous system. In this study, we examined the roles of Group-I mGlu receptors in gastrointestinal function. We investigated the expression of Grm1 (mGlu1) and Grm5 (mGlu5) in the mouse myenteric plexus using RNAscope in situ hybridization. Live calcium imaging and motility analysis were performed on ex vivo preparations of the mouse colon. mGlu5 was found to play a role in excitatory enteric neurotransmission, as electrically-evoked calcium transients were sensitive to the mGlu5 antagonist MPEP. However, inhibition of mGlu5 activity did not affect colonic motor complexes (CMCs). Instead, inhibition of mGlu1 using BAY 36-7620 reduced CMC frequency but did not affect enteric neurotransmission. These data highlight complex roles for Group-I mGlu receptors in myenteric neuron activity and colonic function.
Dispersal ability and biogeographic gradients influence gene flow of 3 aquatic insects in Laurentian Great Lakes interdunal wetlands

Population genetic connectivity is influenced by multiple abiotic and biotic attributes, including geography, dispersal ability, and life history, which may lead to different patterns of population structure of organisms occupying similar habitats. We investigated how differences in dispersal ability and biogeographic gradients correspond with population structuring of 3 aquatic insect species found within naturally fragmented interdunal wetlands along the eastern shoreline of Lake Michigan in midwestern USA. Interdunal wetlands are small, highly fragmented, and patchily distributed along the eastern coast of Lake Michigan, USA. Our focal species, Anax junius (Drury, 1773), Notonecta undulata Say, 1832, and Caenis amica Hagen, 1861 were chosen as high, intermediate, and low dispersers, respectively. We hypothesized that all insect populations experience isolation by distance with relatively low gene flow among sites, but that the strength of isolation by distance varies with dispersal ability. We used cytochrome c oxidase subunit I sequence data to confirm species identification and restriction enzyme-association DNA sequencing for population genomic analyses. Our cytochrome c oxidase subunit I data revealed that Caenis populations consisted of multiple species split along a latitudinal gradient. Restriction site-association DNA sequencing data showed that A. junius displayed strong isolation by distance, where N. undulata did not. Additionally, both A. junius and N. undulata populations displayed 2 genetic clusters along the coastline, and genetic diversity increased along with latitude. These results indicate that biogeographical variables, such as latitude and covarying abiotic factors, may be stronger predictors of population structure than dispersal ability and that inference of population structure within aquatic macroinvertebrates should be on a species-specific basis.
Cell-Laden Marine Gelatin Methacryloyl Hydrogels Enriched with Ascorbic Acid for Corneal Stroma Regeneration

Corneal pathologies from infectious or noninfectious origin have a significant impact on the daily lives of millions of people worldwide. Despite the risk of organ rejection or infection, corneal transplantation is currently the only effective treatment. Finding safe and innovative strategies is the main goal of tissue-engineering-based approaches. In this study, the potential of gelatin methacryloyl (GelMA) hydrogels produced from marine-derived gelatin and loaded with ascorbic acid (as an enhancer of the biological activity of cells) was evaluated for corneal stromal applications. Marine GelMA was synthesized with a methacrylation degree of 75%, enabling effective photocrosslinking, and hydrogels with or without ascorbic acid were produced, encompassing human keratocytes. All the produced formulations exhibited excellent optical and swelling properties with easy handling as well as structural stability and adequate degradation rates that may allow proper extracellular matrix remodeling by corneal stromal cells. Formulations loaded with 0.5 mg/mL of ascorbic acid enhanced the biological performance of keratocytes and induced collagen production. These results suggest that, in addition to marine-derived gelatin being suitable for the synthesis of GelMA, the hydrogels produced are promising biomaterials for corneal regeneration applications.
Formation of the Cl I line at 1351 A in the solar chromosphere

The formation of the multiplet containing the Cl I 1351 A line is investigated due to the unusual brightness of this line. It is determined that this line is formed via a fluorescence effect driven by the 1335 A line of C II. Non-LTE calculations including this effect are found to agree with the observed line intensity. It is shown that the 1347 and 1363 A lines of Cl I do not benefit from this effect and they are predicted to be approximately 100 times weaker.
Identification and Characterization of Preventable Adverse Drug Events in Family Medicine Clinics from Central Saudi Arabia

Background: Medication errors can result in adverse drug events (ADEs) and cause considerable patient harm. Limited data are available from Saudi Arabia and the Middle East regarding the prevalence of preventable adverse drug events (pADEs) in primary care settings. Objectives: To estimate the period prevalence of pADEs and assess the medication error severity in primary care setting in Saudi Arabia. Methods: This retrospective study is a continuation of a previous study where 117 of 2000 adult patients managed at the Family Medicine clinics of King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia, were identified to have had least one medication error in the past 15 months. The electronic health records of these 117 patients were analyzed for a 3-month post-medication error period to explore the presence of pADE. Medication errors were categorized according to the National Coordinating Council for Medication Error Reporting and Prevention index (NCC MERP) and the occurrence of pADE was assessed using the NCC MERP scheme. Results: Of the included 117 patients, 9 (7.7% [95% confidence interval (CI): 2.79–12.59]) experienced pADE (Category E), while 108 (92.3% [95% CI: 87.97–98.35]) did not (Category C). All patients who experienced pADE were using over-the-counter medications and were on polypharmacy. Outcomes 2a and 2b (asthma and β-blocker) accounted for two and four cases, respectively, while Outcomes 6 (warfarin and international normalized ratio), 7 (lithium and lithium level), 16 (new oral anti-coagulant or warfarin and antiplatelet), and 17 (acetylsalicylic acid [aspirin] and antiplatelet) each accounted for one case. Conclusions: This study provides the period prevalence of patients with pADEs from Family Medicine clinics at a major tertiary hospital of Saudi Arabia, and highlights the need for a multicenter study of clinically important medication errors at the prescribing and monitoring stages for the development of quality improvement programs.
Machine learning for predictive model in entrepreneurship research: predicting entrepreneurial action

ABSTRACT This study introduces a method for developing predictive models using machine learning in entrepreneurship research. Machine learning is known to provide a superior performance of prediction by identifying hidden patterns in data through an inductive approach. However, there are very few studies adopting this methodology in social sciences, especially in the field of entrepreneurship. This study investigates the utility of machine learning in entrepreneurship research and proposes a practical method to develop a predictive model using machine learning. For the implementation of this method, as a case study, this study builds a model that predicts entrepreneurial action based on data from the Global Entrepreneurship Monitor (GEM). This study compares the performance of machine learning such as XG boost and artificial neural network (ANN) with traditional statistical method, logistic regression model. Performance indicators such as accuracy, sensitivity, specificity, and area under curve (AUC) were used for evaluation. XG boost showed the highest performance in all indicators except for precision. In the analysis of the variable importance, self-efficacy and opportunity are the most influential factors for predicting entrepreneurial action.
An algorithm for identification in the /spl nu/-gap metric

This paper suggests a method for identification in the /spl nu/-gap metric. For a finite number of frequency response samples, a problem for identification in the /spl nu/-gap metric is formulated and an approximate solution is described. It uses an iterative technique for obtaining an /spl Lscr//sub 2/-gap approximation. Each stage of the iteration involves solving an LMI optimisation. Given a known stabilising controller and the /spl Lscr/2-gap approximation, it is shown how to derive a /spl nu/-gap approximation.
Was Marshall Right? Managerial Failure and Corporate Ownership in Edwardian Britain

Alfred Marshall argued that the malaise of public companies in Edwardian Britain was due to the separation of ownership from control and a lack of professional management. In this paper, we examine the ownership and control of the c.1,700 largest British companies in 1911. We find that most public companies had a separation of ownership and control, but that this had little effect on their performance. We also find that manager characteristics that proxy for amateurism are uncorrelated with performance. Ultimately, our evidence suggests that, if Marshall was correct in identifying a corporate malaise in Britain, its source lay elsewhere.
Non-Destructive Evaluation of Magnetic Anisotropy Associated with Crystallographic Texture of Interstitial Free Steels Using an Electromagnetic Sensor

Interstitial free (IF) steels, as third generation of deep drawing steel, are used in the automotive industry for applications requiring good formability, strength and a superior surface quality. The formability is achieved by having a strong gamma fibre ( γ -fibre) crystallographic texture and is quantified by measuring the r-value from tensile tests. Whereas the crystallographic texture is typically measured using EBSD or XRD on samples after careful surface preparation, it is desirable to be able to evaluate it non-destructively on production material. To do this aim, a magnetic anisotropy measurement is an attractive approach as the magnetic behaviour of steel is known to be affected by its crystallographic texture. A set of interstitial free steel samples, at different stages of recrystallisation process (i.e., commercially cold rolled and annealed to achieve fully and partially recrystallised microstructures) and consequently different texture components have been employed to study the measurement and possibility of model-wise predicting magnetic anisotropy in IF steels. In order to compare the predicted magnetic anisotropy to the measured values, a finite element (FE) microstructure model that takes into account crystallographic texture was applied. The findings indicate that the non-destructive technique deployed in this study - a U-shaped electromagnetic (EM) sensor that can be placed onto a sheet specimen - is promising for a rapid assessment of the magnetic anisotropy in IF steels.
Biomarkers of Intestinal Injury in Colic

Simple Summary Biomarkers are measurable substances within body tissues or fluids that allow for the identification of ongoing injury or disease. Colic secondary to gastrointestinal disease is one of the most frequent causes of morbidity and mortality in horses. Specifically, colic associated with intestinal ischemia is the most life-threatening variety of this disease. Optimization of biomarkers for the diagnosis of colic and identification of intestinal ischemia may expedite the diagnosis and management of this disease and thus help to alleviate this burden on the equid population. Lactate, and specifically the L isomer, is a commonly employed biomarker in colic evaluations. A variety of other biomarkers, however, have been preliminarily evaluated for equine colic. This paper reviews currently explored biomarkers in equine medicine for colic. Ultimately, based on this review, L-lactate continues to be the most reliable marker for intestinal ischemia during colic. However, further exploration of the biomarkers included here may eventually provide the key to accelerated identification, intervention, and thus better outcomes for horses suffering from intestinal ischemia. Abstract Biomarkers are typically proteins, enzymes, or other molecular changes that are elevated or decreased in body fluids during the course of inflammation or disease. Biomarkers pose an extremely attractive tool for establishing diagnoses and prognoses of equine gastrointestinal colic, one of the most prevalent causes of morbidity and mortality in horses. This topic has received increasing attention because early diagnosis of some forms of severe colic, such as intestinal ischemia, would create opportunities for rapid interventions that would likely improve case outcomes. This review explores biomarkers currently used in equine medicine for colic, including acute phase proteins, proinflammatory cytokines, markers of endotoxemia, and tissue injury metabolites. To date, no single biomarker has been identified that is perfectly sensitive and specific for intestinal ischemia; however, L-lactate has been proven to be a very functional and highly utilized diagnostic tool. However, further exploration of other biomarkers discussed in this review may provide the key to accelerated identification, intervention, and better outcomes for horses suffering from severe colic.
Climate teleconnections related to El Niño winters in a lake‐effect region of west‐central New York

A 64‐year climatological record for the cold season in Syracuse, New York is analyzed for temperature and snowfall. Evidence suggests that El Niño winters are characterized by warmer temperatures and below normal snowfall during the first month of winter followed by colder temperatures and above normal snowfall for the second month of winter. Major snow events were more than five times more likely to occur for El Niño winters compared to climatology. It is suggested that the greater frequency of heavy snowfalls is related to both favorable dynamics and warmer lake/ocean temperatures which follow the mild early winter period. Copyright © 2008 Royal Meteorological Society
Optimizing nitrogen fertilization for forage maize production to maximize profit and minimize environmental costs in a rainfed region in China

ABSTRACT Appropriate nitrogen (N) fertilizer application can improve crop yield, increase N-use efficiency and reduce environmental pollution. A two-year field experiment was performed to explore the influences of N fertilizer rates on dry matter yield, crop N removal, N-use efficiency and apparent N budget. Five N application rates, i.e. 0, 70, 140, 210 and 280 kg N ha−1 (referred to as N0, N70, N140, N210 and N280) were tested. The application of fertilizer N markedly increased the dry matter yield of forage maize, which was highest for N210 with a two-year average of 22.6 t ha−1. The N210 treatment increased crop N removal by 34.9% on average compared with N0. As the N rate increased, soil nitrate-N accumulation increased exponentially, apparent N loss increased linearly and agronomic efficiency clearly decreased. The apparent N loss and soil nitrate-N accumulation decreased by 19.4% and 19.0%, respectively, and partial-factor productivity increased by 24.5% under the N rate that maximizes profit (195.1 kg ha−1) in comparison with those under the N rate that maximizes yield (244.4 kg ha−1). The N rate (195.1 kg ha−1) is recommended to maximize profit and minimize environmental costs in forage maize production in the semi-arid region in China.
The nature of NML Cygnus

We suggest that H II region observed near NML Cygnus, a highly evolved mass-losing giant, results from the photoionization of the outflow by the luminous, hot stars in the Cyg OB2 association. NML Cyg is at a projected distance of 100 pc from Cyg OB2, but because all of these objects are near the center of the X-ray emitting superbubble in Cygnus where the hydrogen is mostly ionized, the Lyman continuum photons probably can travel this far without appreciable absorption. The observed structure of the H II region matches this hypothesis quite well. This picture provides the first reasonably precise estimate of the distance to NML Cyg: 2 kpc. At this distance, the star has a luminosity of 5 x 10/sup 5/ L/sub sun/ and a minimum mass loss rate of 6.4 x 10/sup -5/ M/sub sun/ yr/sup -1/. We conclude that NML Cyg is a massive (50 M/sub sun/) star in a highly evolved state. It is likely that it will soon become a supernova and contribute to the general expansion of the superbubble. Before doing so, however, it may endure a phase as a W-R star. If it remains a mass-losing supergiant when it does explode, there maymore » be enough dust around it that it would be optically inconspicuous even if interstellar extinction were negligible. The possiblity that infrared supernovae are common would be very important for understanding the rate of stellar explosions, the formation of pulsars, and nucleosynthesis in the Galaxy. « less
Atomic-resolution imaging of oxidation states in manganites

Aberration corrected electron optics allows routine acquisition of high spatial resolution spectroscopic images in the scanning transmission electron microscope, which is important when trying to understand the physics of transition-metal oxides such as manganites. The physical properties of these perovskites are intimately related to the occupancies of the partially filled $3d$ bands, which define their oxidation state. In this work, we review procedures to obtain this electronic property in ${\text{La}}_{x}{\text{Ca}}_{1\ensuremath{-}x}{\text{MnO}}_{3}$ from atomic-column-resolved electron energy-loss spectra measured in the aberration corrected scanning transmission electron microscope. In bulk samples, several features of both the average $\text{Mn}\text{ }{L}_{2,3}$ edge and the $\text{O}\text{ }K$ edge fine structure change linearly with Mn nominal valence. These linear correlations are extracted and used as a calibration to quantify oxidation states from atomic resolution spectroscopic images. In such images, the same fine-structure features exhibit further changes, commensurate with the underlying atomic lattice. Mn valence values calculated from those images show unexpected oscillations. The combination of experiment with density-functional theory and dynamical scattering simulations allows detailed interpretation of these maps, distinguishing dynamical scattering effects from actual changes in electronic properties related to the local atomic structure. Specifically, in ${\text{LaMnO}}_{3}$, the two nonequivalent O sites can be distinguished by these methods.
An Analysis of Burnout among Female Nurse Educators in Saudi Arabia Using K-Means Clustering

Nurse educators are often burnt out and suffer from depression due to their demanding job settings. Biochemical markers of burnout can provide insights into the physiological changes that lead to burnout and may help us prevent burnout symptoms. Research was conducted using a descriptive cross-sectional survey design and a multi-stage sampling method. The ministry of education website provides a list of Saudi Arabian nursing education programs that offer bachelor of science in nursing programs (BSN). The study consisted of 299 qualified participants. Malsach Burnout Inventory (MBI) was used to measure burnout as the dependent variable. The MBI is a 22-item scale that measures depersonalization, accomplishment, and emotional exhaustion during work. Bootstrapping with 5000 replicas was used to address potential non-normality. During this framework, four deep neural networks are created. They all have the same number of layers but differ in the number of neurons they have in the hidden layers. The number of female nurse educators experiencing burnout is moderate (mean = 1.92 ± 0.63). Burnout is also moderately observed in terms of emotional exhaustion (mean = 2.13 ± 0.63), depersonalization (mean = 2.12 ± 0.50), and personal achievement scores (mean = 12 2.38 ± 1.13). It has been shown that stacking the clusters at the end of a column increases their accuracy, which can be considered an important feature when classifying.