Higher Evaluation Scores Research Articles

Effectiveness of the combination of workshops and flipped classroom model to improve tube fixation training for nursing students.

BACKGROUNDTube indwelling is a key procedure in modern medicine. Careful tube setup is necessary to prevent unplanned extubation. The training for tube fixation is time- and resource-consuming, and optimal modes of training are currently being sought. Previous studies have compared workshops and flipped classroom models separately using conventional teaching strategies, but no study has examined a combination of both teaching models in nursing training.AIMTo compare the effectiveness of workshops vs workshops combined with the flipped classroom model for improving tube fixation training for nursing students.METHODSThis was a prospective cohort study. In this study, 149 nurses who joined our hospital in 2019 underwent training using workshops combined with the flipped classroom model (experimental group), while 159 nurses who joined the hospital in 2018 received only workshop-based training (control group). The combination of workshops with the flipped classroom training model was divided into two modules: pre-class and in-class training. The participation of nurses in the training activities, on-site assessment of training, nurses’ evaluation of their training, and related indicators of tube quality management were evaluated.RESULTSThe average age of nurses in the control group was 22.94 ± 0.94 years and that of nurses in the experimental group was 25.42 ± 3.23 years (P < 0.01). The qualified rate of after-class assessments for the experimental and control groups was 100.00% (average score: 94.01 ± 2.78 points) and 91.82% (average score: 84.24 ± 2.94 points), respectively (P < 0.01). Most nurses in the experimental group completely agreed that the combined training was helpful to cultivate clinical thinking and independent learning ability and to master knowledge of tube fixation. In addition, the training content within the pre-class teaching video, pre-class tube atlas, pre-class main instructor guidance, in-class demonstration, and in-class practice was very informative. The experimental group had higher evaluation scores than the control group (4.88 ± 0.38 vs 4.67 ± 0.64; P < 0.01). Comparison of tube quality management before and after training in 2018 to 2019 revealed that the unplanned ureteral tube removal rate dropped from 0.25‰ to 0.06‰, the unplanned chest tube removal rate dropped from 1.07‰ to 0.78‰, and the unplanned gastric tube removal rate dropped from 0.36‰ to 0.17‰. The incidence rate of pressure ulcers caused by the tube decreased from 0.78‰ to 0.45‰.CONCLUSIONThe combination of workshop and flipped classroom training is effective in improving tube fixation training of nurses, cultivating nurses’ active learning abilities and clinical thinking, and improving the safety of the procedure.

Machine Learning-Based Psychology Evaluation of College Students for Building Innovative Health Service System.

Leadership psychology among college students is a multidimensional concept that primarily encompasses practical ability, teamwork ability, political literacy, emotional intelligence, etc. At present, a common problem among the surveyed college students is their leadership ability is not strong. Reasons include the lack of leadership-related knowledge and leadership awareness among college students as well as the fact that the public, particularly college educators, do not give leadership training a sufficient attention. Leadership psychology is a primary factor influencing the development of college students. It is necessary to strengthen college students' leadership education. This work extracts the key factors that influence the leadership of college students by analyzing their big behavior data. The extracted factors include the theoretical knowledge of leadership, practical ability, leadership psychology, home education, and exercise. To evaluate the degrees of these factors influencing the students' leadership, we develop an advanced multicriteria decision-making framework based on the ENtropy theory and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), called EN-TOPSIS. In EN-TOPSIS, an entropy method is used to determine the criteria weights rather than using the subjective weighting method. Three groups of college students are surveyed and evaluated based on the five factors. Each group contains ten students. The evaluation results show that the leadership of the students is mainly influenced by their practical ability and leadership psychology. The students with the highest evaluation score are recognized as having high levels of leadership.

INFLUENCE OF DRYING TEMPERATURE AND TIME ON QUALITY OF DRIED DRAGON FRUIT PEEL

The study aimed to investigate the effects of drying temperature and time on quality of dried dragon fruit peel. The experiment was arranged with 02 factors (the drying temperatures of 45oC, 50oC, 55oC, 60oC and drying time of 2 hours, 3 hours, 4 hours, 5 hours), 04 levels and 03 repetitions. The evaluated criteria include: moisture, total polyphenol content, anthocyanin, antioxidant activity, and sensory evaluation of the product. The results showed that at 50oC for 3 hours, polyphenols total content was 2578 mgGAE/100g dry matter, antioxidant activity was 29.76%, anthocyanin content 0.46%, high Sensory evaluation scores for colour (5.80), flavour (5.87), taste (6.17) and texture (6.22).

Spacious Environments Make Us Tolerant-The Role of Emotion and Metaphor.

The physical environment plays an important role in moral cognition. Previous research has demonstrated that the physical environment affects individual moral judgment. Investigators have argued that the environment influences moral judgment through emotion and cognition, such as during metaphor processing. Following the intensification of urbanization and increases in population size, the phenomenon of a narrow environment has become more common. However, the relation between environmental spaciousness and moral judgment has not been thoroughly examined. We examined the effect of environmental spaciousness (spaciousness vs. narrowness) on moral judgments in Experiment 1 and Experiment 2. Results showed that participants report a higher rating score of moral judgment in more spacious environments compared with narrow environments. We further explored the roles of emotion and metaphor in the relation between environmental spaciousness and moral judgments. We found support for a partial mediation effect of emotion in the relationship between environmental spaciousness and moral judgment. The results also supported an association between the concept of spaciousness and tolerant cognition. Spacious environments may elicit positive emotions and more tolerant cognition, which in turn influences moral judgment. These results provide new evidence for the influence of the environment on moral judgments, and more attention may be warranted to incorporate this relationship in environmental design.

Teacher Evaluation Policy Enactment: Understanding the Relationship among Principal Characteristics, Teacher Perspectives, and Institutional Environments

ABSTRACT Based extensive fieldwork and interviews, this study explains teacher evaluation policy enactment as shaped by three conditions: 1) a principal’s backgrounds, role perceptions, knowledge of instruction, and interactions with teachers; 2) teachers’ perspectives; and 3) environmental demands. Notably, principals may lack the backgrounds, knowledge of teaching, role perceptions, and established interactional routines to engage teachers about their instruction in evaluative contexts. Furthermore, the teachers’ press to earn high evaluation scores may inhibit instructional leadership, as teachers become focused on the accountability aspects of evaluation rather than on the affordances of evaluation reforms to help them improve.

Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana

Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 &gt; 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines.

The impact of self-evaluation of public school principals in Jordan on leadership practices and the quality of education from the point of view of school principals themselves – A field study on secondary school principals – Amman –: أثر التقييم الذاتي لمديري المدارس الحكومية في الأردن على الممارسات القيادية وجودة التعليم من وجهة نظر مديري المدارس أنفسهم – دراسة ميدانية

This study aimed to identify the impact of self-evaluation of secondary school principals in the Kasbah of Oman on leadership practices and the quality of education from the point of view of the principals themselves. The sample of the study consisted of all (39) secondary school principals of the Directorate of Education of the Kasbah of Amman. The results of the study showed that the overall arithmetic average of the study sample estimates for the effect of self-evaluation of school principals was (4.14) with a high evaluation score. The mean dimensions of the self-evaluation ranged between (3.82 - 4.14), where it came after the quality of leadership practices ranked first with an average of (4.14). With a high evaluation score, followed by second place after the quality of the learning process with an arithmetic average (4.00) and a high evaluation score, and in third place the quality of teaching with an arithmetic average (3.94) and a high evaluation score, and in the fourth place the quality of students' performance with an arithmetic average (3.84) and a high assessment score. , And last ranked c Dah school environment, an arithmetic mean (3.82) and highly evaluated high, These results indicate that there is a high impact of self-evaluation among secondary school principals in the Kasbah of Oman and education on leadership practices and quality of education. In light of these results, the study recommended the importance of self-evaluation of administrative and educational leadership practices and the quality of education.

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 s and no pre-processing requirements.We trained and validated MU-Net on 128 T2-weighted mouse MRI volumes as well as on the publicly available MRM NeAT dataset of 10 MRI volumes. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). Further, we explored the effectiveness of our network in the presence of different architectural features, including skip connections and recently proposed framing connections, and the effects of the age range of the training set animals.These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net.

AHP기법을 이용한 천연가스 발전소의 최적가용기술의 중요도 선정

Recently, the Act on the Integrated Management of Environmental Pollution Facilities was implemented in Korea. Under the integrated management, the license period of the workplace will be renewed by five to eight years through the assessment of the level of environmental management of the workplace in accordance with the integrated law implemented in 2017. In this process, the application rate of the optimal use technique is evaluated, which is essential to obtain a high evaluation score. However, there is a problem in assessing optimal use techniques without considering the situation at the workplace. An AHP analysis was conducted on experts according to the situation of the workplace by examining the applicable optimal use techniques and the EU standards, and the priority was prepared by selecting the techniques to be applied first.

Ecological niche models improve home range estimations

AbstractHome ranges in animals can be estimated by different methods like minimum convex polygons, characteristic hulls or kernels while correlative ecological niche models (ENMs) are commonly employed for forecasting species' ranges. However, ENMs can also model the distribution of individuals if environmental very high spatial resolution data are available. Indeed, remote sensing (RS) can provide images with pixel sizes of few centimetres. Here, we modelled the distribution of individual lizards (Podarcis bocagei) combining aerial‐like photographs recorded with a compact camera and a matrix of temperature/humidity data‐loggers to obtain several environmental layers with very high spatial resolution. We recorded lizards’ positions in a 20 × 20 m mesocosm with a high precision GPS device (~10 cm of error), multiple times per day throughout the whole period of daily activity. We built an orthophoto map (pixels of 20 cm2) from camera pictures, a digital surface model, and a land‐cover supervised classification map. We recreated climate‐like variables by combining data‐logger data. For each individual, we calculated the distance to males and females, excluding the focal lizard. We computed individual realized niche models with Bioclim, GAM, GLM, Maxent and random forest. Models attained a very high evaluation score in most cases. The most contributing variables were related to microclimate (isothermality, minimum temperature and humidity) and distance to conspecifics. Our very high spatial resolution models provided robust information on how space is used by each lizard. Correlative models can identify the most suitable areas inside the home range, similar to core areas estimated from kernel algorithms, but allowed better statistical inference. Overall, RS tools generated high‐quality environmental data, and when combined with ENMs, improved the robustness of the predictions on spatial patterns of small terrestrial animals.

Too Late! Influence of Temporal Delay on the Neural Processing of One's Own Incidental and Intentional Action-Induced Sounds.

The influence of delayed auditory feedback on action evaluation and execution of real-life action-induced sounds apart from language and music is still poorly understood. Here, we examined how a temporal delay impacted the behavioral evaluation and neural representation of hurdling and tap-dancing actions in a functional magnetic resonance imaging (fMRI) experiment, postulating that effects of delay diverge between the two, as we create action-induced sounds intentionally in tap dancing, but incidentally in hurdling. Based on previous findings, we expected that conditions differ regarding the engagement of the supplementary motor area (SMA), posterior superior temporal gyrus (pSTG), and primary auditory cortex (A1). Participants were videotaped during a 9-week training of hurdling and tap dancing; in the fMRI scanner, they were presented with point-light videos of their own training videos, including the original or the slightly delayed sound, and had to evaluate how well they performed on each single trial. For the undelayed conditions, we replicated A1 attenuation and enhanced pSTG and SMA engagement for tap dancing (intentionally generated sounds) vs. hurdling (incidentally generated sounds). Delayed auditory feedback did not negatively influence behavioral rating scores in general. Blood-oxygen-level-dependent (BOLD) response transiently increased and then adapted to repeated presentation of point-light videos with delayed sound in pSTG. This region also showed a significantly stronger correlation with the SMA under delayed feedback. Notably, SMA activation increased more for delayed feedback in the tap-dancing condition, covarying with higher rating scores. Findings suggest that action evaluation is more strongly based on top–down predictions from SMA when sounds of intentional action are distorted.

Artificial Intelligence in cardiovascular medical imaging

Artificial Intelligence (AI) has become an important tool for computer-aided diagnosis of medical imaging. This review aims to provide an overview for clinicians, explaining the relevant aspects of artificial intelligence and machine learning (ML) and presenting up-to-date applications of AI and ML techniques to medical imaging methods such as angiography, magnetic resonance, and echocardiography. For each imaging method, we present the acquisition process, the types of diagnostic test interpretation, and the challenges related to them, as well as how AI/ML techniques, have improved the process of decision making. A summary of selected works applying AI/ML techniques to medical imaging is organized into a table, which highlights the scope of the study, the dataset used, the details of each approach as well as the measured results, including objectives and criteria. The overall benefits of AI in medical imaging are extracted based on the diverse applications and high evaluation scores. In the end, cardiologists should have an advanced understanding of using AI to integrate clinical data and making the final decision in diagnosis.

Study on IoT Evacuation Support System in Event of Disasters

In this study, we implemented an Internet of Things (IoT) evacuation support system using beaconsthat supports evacuees during disasters. This system realized quick and accurate registration of safetyinformation and management of evacuation centers without requiring special equipment by using amobile device such as a smartphone and a beacon. Additionally, through safety information registration,this system also supports vulnerable people such as the elderly and children by implementinga proxy safety information registration function. Safety information registered at evacuation centersis collected into a regional disaster prevention information/safety information management systemmanaged by the emergency response headquarters. Furthermore, this system also provides regionalinformation and disaster prevention information during normal use. To evaluate the IoT evacuationsupport system, we evaluated the system usability scale and the operability, relevance, and effectivenessof each function and many of the functions received high evaluation scores.

Academic Rhinologists' Online Rating and Perception, Scholarly Productivity, and Industry Payments.

The emergence of popular online rating websites, social media platforms, and public databases for industry payments and scholarly outputs provide a complete physician online presence which may guide choice and satisfaction. Websites of all U.S. otolaryngology academic institutions were queried for fellowship-trained rhinologists. Additional well-known and academically active rhinologists were identified by the senior author. Online ratings and comments were collected from Google, Healthgrades, Vitals, and RateMD websites, and weighted rating scores (RS) were calculated on a 1-5 scale. A total of 210 rhinologists with 16 ± 9 years of practice were included, where 6901 online ratings (33 ± 47 per rhinologist) provided an average RS of 4.3 ± 0.6. RS was not different according to gender (p = 0.58), geographic quartile (p = 0.48), social media presence (p = 0.41), or attending top-ranked medical school (p = 0.86) or residency programs (p = 0.89). Years of practice negatively correlated with RS (R = -0.22, p<0.01), and academic ranking significantly influenced RS, with professors, associate professors, and assistant professors scoring 4.1 ± 0.6, 4.3 ± 0.4, and 4.4 ± 0.6, respectively (p = 0.03). Of the 3,304 narrative comments analyzed (3.1 ± 11.6 per rhinologist), 76% (positive) and 7% (negative) had elements of clinical knowledge/outcomes, 56% (positive) and 7% (negative) of communication/bedside manner, and 9% (positive) and 7% (negative) of office staff, cost, and wait-time. All negative comment categories had moderate negative correlation with RS, while positive comment categories regarding knowledge/competence and bedside manner weakly correlated with higher RS. Number of publications (48 ± 54) positively correlated with 2018 industry payments ($11,384 ± $19,025) among those receiving industry compensation >$300 (n = 113). Attending a top-ranked medical school was associated with higher industry payments (p<0.01) and H-index (p = 0.02). Academic rhinologists' online RS was not associated with gender, geographic location, or attending a top-ranked training program, and their scholarly productivity was significantly correlated with total industry payments.

Data Augmentation for Internet of Things Dialog System

With rapid development of voice control technology, making speech recognition more precisely in various IoT domains have been an intractable problem to be solved. Since there are various conversation scenes, understanding the context of a dialog scene is a key issue of voice control systems. However, the reality is available training data for dialog system are always insufficient. In this paper, we mainly solve the problem of data lacking in dialog systems by data augmentation technique. A Generative Adversarial Network(GAN)-based model is proposed and the data are augmented effectively. It can generate from text to text, enhance the original data with text retelling, and improve the robustness of parameter estimation of unknown data by using the sample data generated by GAN model. A new N-gram language model is used to evaluate multiple recognition candidates of speech recognition, and the candidate sentences with the highest evaluation scores are selected as the final result of speech recognition. Our data enhancement algorithm based on the Generative Model is verified by the experiments. In the result of model comparison test, the error rates of data set THCHS30 and AISHELL are 3.3% and 5.1% which are lower than that of the baseline system.

Optic disc and optic cup segmentation based on anatomy guided cascade network

Background and ObjectiveGlaucoma, a worldwide eye disease, may cause irreversible vision damage. If not treated properly at an early stage, glaucoma eventually deteriorates into blindness. Various glaucoma screening methods, e.g. Ultrasound Biomicroscopy (UBM), Optical Coherence Tomography (OCT), and Heidelberg Retinal Scanner (HRT), are available. However, retinal fundus image photography examination, because of its low cost, is one of the most common solutions used to diagnose glaucoma. Clinically, the cup-to-disk ratio is an important indicator in glaucoma diagnosis. Therefore, precise fundus image segmentation to calculate the cup-to-disk ratio is the basis for screening glaucoma. MethodsIn this paper, we propose a deep neural network that uses anatomical knowledge to guide the segmentation of fundus images, which accurately segments the optic cup and the optic disc in a fundus image to accurately calculate the cup-to-disk ratio. Optic disc and optic cup segmentation are typical small target segmentation problems in biomedical images. We propose to use an attention-based cascade network to effectively accelerate the convergence of small target segmentation during training and accurately reserve detailed contours of small targets. ResultsOur method, which was validated in the MICCAI REFUGE fundus image segmentation competition, achieves 93.31% dice score in optic disc segmentation and 88.04% dice score in optic cup segmentation. Moreover, we win a high CDR evaluation score, which is useful for glaucoma screening. ConclusionsThe proposed method successfully introduce anatomical knowledge into segmentation task, and achieve state-of-the-art performance in fundus image segmentation. It also can be used for both automatic segmentation and semiautomatic segmentation with human interaction.

Using Neural Networks for Relation Extraction from Biomedical Literature.

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.

SuVashantor: English to Bangla Machine Translation Systems

This paper presents the system description of Machine Translation (MT) systems for English-Bangla language pair. Our goal was to create two benchmark MT systems that produce a better quality translation and comparatively higher evaluation score than existing MT systems for English to Bangla. In our experiments, we implemented two baseline MT systems using both statistical and neural methods for the said language pair. Our phrase-based statistical model and 2-layer LSTM neural model were trained and evaluated with a large dataset that is carefully pre-processed and contains unique training data to avoid biases from the cross-validation and test data. We achieved the highest scoring BLEU for our experiments with these setups. Furthermore, we improved the performance of the neural model using pre-trained embedding and synthetic monolingual data which are cutting-edge technology for neural models.

Systemic lupus erythematosus in the intensive care unit: a systematic review.

Systemic lupus erythematosus (SLE) is an autoimmune disease with heterogeneous pathophysiologic mechanisms and diverse clinical manifestations. SLE is a frequent cause of intensive care unit (ICU) admissions. Multiple studies with controversial findings on the causes, evolution and outcomes of ICU-admitted patients with SLE have been published. The aim of this paper is to review the literature reporting the clinical characteristics and outcomes, such as mortality and associated factors, in such patients. Among the main causes of ICU admissions are SLE disease activity, respiratory failure, multi-organ failure and infections. The main factors associated with mortality are a high Acute Physiology and Chronic Health Evaluation (APACHE) score, the need for mechanical ventilation, and vasoactive and inotropic agent use. Reported mortality rates are 18.4%-78.5%. Therefore, it is important to evaluate SLE disease severity for optimizing clinical management and patient outcomes.

On a method for location and mobility analytics using location-based services: a case study of retail store recommendation

PurposeLocation-based services (LBS) have become an effective commercial marketing tool. However, regarding retail store location selection, it is challenging to collect analytical data. In this study, location-based social network data are employed to develop a retail store recommendation method by analyzing the relationship between user footprint and point-of-interest (POI). According to the correlation analysis of the target area and the extraction of crowd mobility patterns, the features of retail store recommendation are constructed.Design/methodology/approachThe industrial density, area category, clustering and area saturation calculations between POIs are designed. Methods such as Kernel Density Estimation and K-means are used to calculate the influence of the area relevance on the retail store selection.FindingsThe coffee retail industry is used as an example to analyze the retail location recommendation method and assess the accuracy of the method.Research limitations/implicationsThis study is mainly limited by the size and density of the datasets. Owing to the limitations imposed by the location-based privacy policy, it is challenging to perform experimental verification using the latest data.Originality/valueAn industrial relevance questionnaire is designed, and the responses are arranged using a simple checklist to conveniently establish a method for filtering the industrial nature of the adjacent areas. The New York and Tokyo datasets from Foursquare and the Tainan city dataset from Facebook are employed for feature extraction and validation. A higher evaluation score is obtained compared with relevant studies with regard to the normalized discounted cumulative gain index.