Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment nursing role of care provided to woman with placenta previa and their satisfaction in woman’s Health Hospital

Abstract Back ground: Placenta previa is ones of the further dangerous complexities during pregnancy and is associated with several adverse maternal and fetal-neonatal complications. Aim: Assess peri partum nursing role for the woman with placenta previa and their satisfaction level, Study design: Descriptive study . This studies were conducted at Woman’s Health Hospital, Assiut University, Emergency department and inpatient department. Sample: A convenience sample, 50 women with placenta previa Tools: Structured Interview Questionnaire& observation checklist were used. Results: Finding revealed that the Mean age was.31. 16, all women was multiparty and their mean gestational age was.34. 66 WKs, more than one quarter of women had hysterectomy, more than half of woman had abnormal fetal outcome and more than half of woman are dissatisfaction about care introduced. Conclusion: Nurses have very crucial role in the treatment of placenta previa, integration of nursing staff in the treatment of women with placenta previa is valuableand helps proper management. The maternal and fetal complication is high in the women who receiving the nursing care, so the patient’s satisfaction regarding care introduced is less. Recommendations: Applied integrated nursing care would improve treatment success rate, decrease complications to the mother and fetal and upgrade nursing quality.
Do informal reasoning fallacies really shape decisions? Experimental evidence

Informal reasoning fallacies belong to a persuasive tactic, leading to a conclusion that is not supported by premises but reached through emotions and/or misleading and incomplete information. Previous research focused on the ability to recognize informal reasoning fallacies. However, the recognition itself does not necessarily mean immunity to their influence on decisions made. An experiment was designed to study the relationship between the presence of informal reasoning fallacies and a consequent decision. Having conducted paired comparisons of distributions, we have found some support for the hypothesis that informal reasoning fallacies affect decision-making more substantially than non-fallacious reasoning—strong support in the case of a slippery slope, weak in that of appeal to fear, anecdotal evidence argument defying evaluation. Numeracy and cognitive reflection seem to be associated with higher resistance to the slippery slope, but do not diminish appeal to fear.
Business ecosystem embeddedness to enhance supply chain competence: the key role of external knowledge capacities

Abstract Interest in business ecosystems has grown exponentially over the last decade. This article focuses on the operational benefits of business ecosystems by investigating how embeddedness in business ecosystems influences supply chain competence. Specifically, it considers the mediating effect of external knowledge capacities (i.e. absorptive, desorptive and connective capacity). Data from 271 European firms in business ecosystems was collected to test the paper’s hypotheses using regression analysis with bootstrapping. Results indicate that business ecosystem embeddedness does not in itself improve supply chain competence. Rather, the relationship is explained through (i) absorptive and desorptive capacity as direct mediators; and (ii) connective capacity, which enhances supply chain competence indirectly by improving external knowledge retention for absorptive and desorptive capacity. To the best of our knowledge, this is the first paper to demonstrate benefits of being embedded in business ecosystems other than in terms of innovation. Newly validated scales for business ecosystem embeddedness and connective capacity are provided.
CRISPR/Cas9-mediated pou4f3 knockout induces defects in the development of the zebrafish inner ear

Abstract Objective: The zebrafish is an excellent model for studying gene function in auditory system development. Pou4f3 plays an important role in mouse hair cell formation. Here, we constructed a pou4f3-knockout Tg(Brn3c:GFP) zebrafish to provide an efficient fluorescence-visualized model for studying the molecular mechanisms of ear development. Methods: Cas9/single guide RNAs targeting exon 2 of pou4f3 were designed and injected into one-cell stage zebrafish embryos (G0 generation). The G0 generation were crossed with Tg(Brn3c:GFP) zebrafish to obtain pou4f3-mutant Tg(Brn3c:GFP) zebrafish. The targeting efficiency was detected by polymerase chain reaction amplification and Sanger sequencing. Zebrafish hair cells were observed by laser scanning confocal microscopy in vivo. The morphology of the otoliths and semicircular canals were analyzed. All animal experiments were approved by the Animal Care and Use Committee of Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University (approval No. 2016-KY-040) on March 3, 2016. Results: The pou4f3-mutant Tg(Brn3c:GFP) zebrafish line was successfully established. Fluorescence observation suggested that hair cell development was delayed in pou4f3-knockout zebrafish. Knockout of pou4f3 also induced defects in the otoliths and semicircular canals and impaired ear function in zebrafish. Conclusion: A CRISPR/Cas9-mediated pou4f3 mutant Tg(Brn3c:GFP) zebrafish model was established for the first time to demonstrate the essential role of pou4f3 in zebrafish ear development. Our study provides a highly efficient method for the establishment of a visualized model of gene knockout zebrafish and has the potential to allow high-throughput drug screening to explore therapeutics for related diseases.
Effect of Problem-based Learning on Agreement of Severity Classification Results of Triage Nurses in the Emergency Department

BackgroundProblem-based learning is a learner-led learning method that helps improve critical thinking, problem solving skills, and knowledge. It is necessary to confirm whether it can help to agree the severity classification results among nurses through problem-based learning.MethodsThis study had a single-group time series design to investigate the effect of problem-based learning led by triage nurses on the agreement of Korean Triage and Acuity Scale classification results for patients who visited the emergency department. We extracted 300 patients each in May and August 2018 before problem-based learning began and 300 patients each in May and August 2019 after problem-based learning. ResultsAfter problem-based learning, the length of emergency department stay decreased about 30 minutes, although the decrease was not statistically significant (p=.172). However, self-efficacy for the classification of emergency patients in triage nurses and weighted kappa coefficients were improved (p<.001). ConclusionIn this study, problem-based learning led by triage nurses improved the inter-rater agreement of Korean Triage and Acuity Scale classification results and self-efficacy of triage nurses. Therefore, problem-based learning led by triage nurses can contribute to patient safety in the emergency department by enhancing the expertise of triage nurses and increasing the accuracy of triage classification.
ASSERTIVE ILLOCUTIONARY ACTS STUDY OF BRITISH AND KOREAN IN KOREAN ENGLISHMAN YOUTUBE CHANNEL

This study discusses the use of assertive illocutionary acts uttered by Beckham and Heung-Min in the videos of Korean Englishman YouTube Channel. The study aims to discover the types and differences of assertive illocutionary acts used by the British, represented by Beckham, and Korean, represented by Heung-Min. This research is descriptive qualitative research as it is designed to get in-depth understanding and analysis to describe the pattern of assertive illocutionary acts in British Culture and Korean Culture. British and Korean use assertive acts in their communication, but they use it differently. The differences occur because of the cultural background differences. British tend to use assertive acts more directive. Korean mostly use assertive acts in the manner of thinking about other people's pride.
Forest fragmentation and its potential implications for the management of the Tarumã-Açu River basin, Central Amazon, Brazil

The intensification of deforestation and the consequent fragmentation of the natural landscape in urban and periurban watersheds affect the entire eco-hydrological system, increasing the need to understand how these changes can affect their sustainability. In this sense, the present study evaluated the potential implications of forest fragmentation for the management of the Tarumã-Açu basin, based on the characterization of the structural and functional patterns of the landscape. For this, we mapped and categorized the basin’s forest fragments, based on the supervised classification (Bhattacharyya Method) of Landsat/OLI image, and, subsequently, we calculated the landscape metrics (area, density and size, edge, form, core, isolation and connectivity). The metrics showed a very fragmented landscape, especially in the region of the basin's low course, which concentrates the smallest, most dispersed, and vulnerable fragments even in conservation units. The headwater region, on the other hand, has the largest patches, with a large amount of central area and high structural and functional connectivity, which are fundamental for the sustainability of the basin and, therefore, deserve attention and prioritization by managers. The results offer important subsidies and unpublished data that can contribute to elaboration of the basin’s management plan and for the definition of conservation and restoration strategies of the forest remnants, indicating priority areas for the implementation of these actions.
Differential Transform Method as an Effective Tool for Investigating Fractional Dynamical Systems

This study investigates the use of the differential transform method (DTM) for integrating the Rössler system of the fractional order. Preliminary studies of the integer-order Rössler system, with reference to other well-established integration methods, made it possible to assess the quality of the method and to determine optimal parameter values that should be used when integrating a system with different dynamic characteristics. Bifurcation diagrams obtained for the Rössler fractional system show that, compared to the RK4 scheme-based integration, the DTM results are more resistant to changes in the fractionality of the system.
High‐Resolution Ambient Noise Imaging of Geothermal Reservoir Using 3C Dense Seismic Nodal Array and Ultra‐Short Observation

Tomographic imaging based on long‐term ambient seismic noise measurements, mainly the phase information from surface waves, has been shown to be a powerful tool for geothermal reservoir imaging and monitoring. In this study, we utilize seismic noise data from a dense nodal array (192 3C nodes within 20 km2 ) over a ultra‐short observation period (4.7 days) to reconstruct surface waves and determine the high‐resolution (0.2 km) three‐dimensional (3‐D) S wave velocity structure beneath a rural town in Zhejiang, China. We report the advantage of cross‐coherence over cross‐correlation in suppressing pseudo‐arrivals caused by persistent sources. We use ambient noise interferometry to retrieve high quality Rayleigh waves and Love waves. Body waves are also observed on the R‐R component interferograms. We apply phase velocity dispersion measurements on both Rayleigh waves and Love waves and automatically pick more than 23,000 dispersion curves by using a Machine Learning technique. 3‐D surface wave tomographic results after depth inversion indicate low‐velocity anomalies (between −1% and −4%) from the surface to 2 km depth in the central area. Combined with the conductive characteristics observed on resistivity profile, the low‐velocity anomalies are inferred to be a fluid saturated zone of highly fractured rock. Joint interpretation based on horizontal‐to‐vertical spectral ratio (HVSR) measurements, and existing temperature and fluid resistivity records observed in a nearby well, suggests the existence of the high‐temperature geothermal field through the fracture channel. Strong correlation between HVSR measurements and the S wave velocity model highlights the potential of extraction of both amplitude and phase information from ambient noise.
Implementation of Good Governance in Komodo National Park Management: A Case Study

This study examines the implementation of good governance at the Komodo National Park to provide a specific, in-depth, complex, and explorative understanding of it. The research methodology used was a descriptive interpretive Yin case study approach. Data were obtained through observation, interviews, and documentation. The results revealed that the Komodo National Park applies the principles of good governance, transparency, accountability, responsibility, independence, and fairness. Study findings also contribute benefits, ideas, and studies, which provide reference for and contribution to the management and stakeholders in conducting governance roles, especially for National Park management. Keywords: good governance, management, national park
Development of a predictive model for benthic macroinvertebrates by using environmental variables for the biological assessment of Korean streams

Abstract Predictive models for benthic macroinvertebrates based on changes in environmental variables can assess the biological integrity of streams by comparing observed biotic communities with those expected at reference sites. To develop a predictive model of the abiotic community, we used benthic macroinvertebrates and environmental variables collected from 2,700 sites from 2010 to 2019. First, we selected 357 reference sites by using the 5-day biochemical oxygen demand, ammonia nitrogen, total phosphorus, turbidity, and coarse particle percentage. Then, we used Two-Way Indicator Species Analysis to classify the reference sites into six groups based on benthic macroinvertebrates. Reference sites classified by biological characteristics were linked to environmental variables by multi-discriminant analysis. The relative influences environmental variables on the classified groups were in decreasing order of catchment area, latitude, velocity, water depth, altitude, and longitude. To develop the predictive model, we combined (1) identification level, (2) grouping method, and (3) probability of capture, and then used the normalized root mean square error (NRMSE) to check the fit of each model. The higher the probability of capture was at the family level compared to the species level, the lower was the NRMSE. The grouping method was not as consistent as the identification level and probability of capture because the NRMSE for the number of taxa was low when used as a weighted average. The NRMSE was also low for the Benthic Macroinvertebrates Index and the Benthic Macroinvertebrates Family-level Biotic Index (BMFI) when used for assignment to the group with the highest probability. We selected the predictive model which used family level, weighted average, and BMFI-proposed indicator taxa as the final assessment model due to its sensitivity and fit. This model was the most reasonable choice, but we had to reduce the error of the model and revise it elaborately by securing additional environmental variables.
Adaptación argentina de la nueva escala de Orientación a la Dominancia Social (SDO7)

El presente trabajo adaptó la séptima escala de Dominancia Social en Argentina en sus dos versiones: larga (16 ítems) y corta (ocho ítems; Ho et al., 2015). Se estudiaron las propiedades psicométricas de la escala y se evaluó su estructura factorial. Se analizan las diferencias en SDO en cuanto a sexo, edad, posicionamiento socioeconómico y orientación política. Se trabajó con una muestra no probabilística intencional (n = 1573, escala corta; n = 590, escala larga) entre 18 y 60 años. La escala exhibe niveles de confiabilidad consistentes con la teoría. El análisis factorial confirma la estructura bifactorial de la escala, encontrando dos dimensiones: SDO-Dominante y SDO-E (Anti)Igualitarismo. Se encontró una correlación positiva entre el posicionamiento político de derecha y el SDO.
Estimation and Compensation of Ionospheric Phase Delay for Multi-Aperture InSAR: An Azimuth Split-Spectrum Interferometry Approach

Multiple aperture interferometric synthetic aperture radar (MAI) can measure ground displacements along SAR track. However, MAI measurements may suffer from severe ionospheric error, especially for long-wavelength SAR sensors. This study presents a new approach, azimuth split-spectrum interferometry (AziSSI), to correct ionospheric errors in MAI measurements. The proposed method can straightforwardly resolve ionospheric phase delay by exploiting two subband MAI interferograms with different centroid frequencies. We utilized two groups of ALOS-1 PALSAR images, which cover the 2008 Wenchuan earthquake containing strong coseismic ground deformation and a stable coast region in Chile, respectively, to test the proposed method. Experimental results show that the AziSSI method can successfully recover the coseismic displacements induced by the Wenchuan earthquake and circumvent the azimuth stripes for the Chile case’s MAI measurements. The maximum ionosphere-induced azimuth shifts are about 2.2 m for the Wenchuan case and 2.5 m for the Chile case. We validated the correction performance of the AziSSI method with the Wenchuan case by comparing the compensated azimuth displacements with the simulated deformation from a forward fault slip model and coseismic global positioning system (GPS) measurements. The validations show that the deformation patterns after the ionospheric correction are consistent with the simulations of the sinistral slip of the causative fault. The root-mean-square error between the GPS and MAI measurements is reduced from 0.77 to 0.28 m before and after the ionospheric correction, indicating that AziSSI can significantly compensate ionospheric errors for MAI.
Strengthening Rice Farmers' Social Capital in Increasing Agricultural Productivity

This study aims to determine the pattern of strengthening social capital in increasing agricultural productivity in Tridana Mulya Village. The approach used in this study is a qualitative approach with data collection techniques including interviews, observation, and documentation. In this study, 19 rice farmers were used as informants. The results showed that the strategy of strengthening the social capital of rice farmers in increasing agricultural productivity in Tridhana Mulya Village included mutual trust and cooperation. The mutual trust of rice farmers in Tridhana Mulya Village is in the form of providing agricultural business capital, increasing the work ethic of rice farmers, increasing farmers' tenacity in farming, and allocation of working hours by farmers. While strengthening social capital through cooperation, namely cooperation in maintaining facilities and infrastructure, cooperation in working on rice fields and the use of livestock manure for fertilizer, cooperation in maintaining agricultural hygiene, and cooperation in maintaining the cleanliness of agricultural irrigation.
Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network

Bike-sharing systems have made notable contributions to cities by providing green and sustainable mobility service to users. Over the years, many studies have been conducted to understand or anticipate the usage of these systems, with the hope to inform their future developments. One important task is to accurately predict usage patterns of the systems. Although many deep learning algorithms have been developed in recent years to support travel demand forecast, they have mainly been used to predict traffic volume or speed on roadways. Few studies have applied them to bike-sharing systems. Moreover, these studies usually focus on one single dataset or study area. The effectiveness and robustness of the prediction algorithms are not systematically evaluated. In this study, we propose a Spatial-Temporal Memory Network (STMN) to predict short-term usage of bicycles in bike-sharing systems. The framework employs Convolutional Long Short-Term Memory models and a feature engineering technique to capture the spatial-temporal dependencies in historical data for the prediction task. Four testing sites are used to evaluate the model. These four sites include two station-based systems (Chicago and New York) and two dockless bike-sharing systems (Singapore and New Taipei City). By assessing STMN with several baseline models, we find that STMN achieves the best overall performance in all the four cities. The model also achieves superior performance in urban areas with varying levels of bicycle usage and during peak periods when demand is high. The findings suggest the reliability of STMN in predicting bicycle usage for different types of bike-sharing systems.
Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios

The purpose of this paper was to predict the path loss characterization of the ground-to-air (G2A) communication channel between the ground sensor (GS) and unmanned aerial vehicle (UAV) using machine learning (ML) models in smart farming (SF) scenarios. Two ML algorithms such as support vector regression (SVR) and artificial neural network (ANN) were studied to analyze the measured data in different scenarios with Napier and Ruzi grass farms as the measurement locations. The proposed empirical GS-to-UAV two-ray (GUT-R) model and the ML models were compared to characterize path loss prediction models. The performances of the path loss prediction models were evaluated using the statistical error indicators in different measurement locations and UAV trajectories. To obtain the statistical error indicators, the accuracy path loss results of UAV trajectory at 2 m altitudes showed the SVR model (MAE = 1.252 dB, RMSE = 3.067 dB, and R2 = 0.972) and the ANN model (MAE = 1.150 dB, RMSE = 2.502 dB, and R2 = 0.981) for the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 1.202 dB, RMSE = 2.962 dB, and R2 = 0.965) and the ANN model (MAE = 1.146 dB, RMSE = 2.507 dB, and R2 = 0.983) were presented. For UAV trajectory at 5 m altitudes, the SVR model (MAE = 2.125 dB, RMSE = 4.782 dB, and R2 = 0.933) and the ANN model (MAE = 2.025 dB, RMSE = 4.439 dB, and R2 = 0.950) were resulted in the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 2.112 dB, RMSE = 4.682 dB, and R2 = 0.935) and the ANN model (MAE = 2.016 dB, RMSE = 4.407 dB, and R2 = 0.954) were displayed. The proposed ML models using SVR and ANN can optimally predict the path loss characterization in SF scenarios, where the accuracy was 95% for the SVR and 97% for the ANN.