Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Exploration of Main Elements for Systematic Classification Development: Case Study of Patient Safety Incidents

Background Currently, there is no holistic theoretical approach available for guiding classification development. On the basis of our recent classification development research in the area of patient safety in health information technology, this focus area would benefit from a more systematic approach. Although some valuable theoretical and methodological approaches have been presented, classification development literature typically is limited to methodological development in a specific domain or is practically oriented. Objective The main purposes of this study are to fill the methodological gap in classification development research by exploring possible elements of systematic development based on previous literature and to promote sustainable and well-grounded classification outcomes by identifying a set of recommended elements. Specifically, the aim is to answer the following question: what are the main elements for systematic classification development based on research evidence and our use case? Methods This study applied a qualitative research approach. On the basis of previous literature, preliminary elements for classification development were specified, as follows: defining a concept model, documenting the development process, incorporating multidisciplinary expertise, validating results, and maintaining the classification. The elements were compiled as guiding principles for the research process and tested in the case of patient safety incidents (n=501). Results The results illustrate classification development based on the chosen elements, with 4 examples of technology-induced errors. Examples from the use case regard usability, system downtime, clinical workflow, and medication section problems. The study results confirm and thus suggest that a more comprehensive and theory-based systematic approach promotes well-grounded classification work by enhancing transparency and possibilities for assessing the development process. Conclusions We recommend further testing the preliminary main elements presented in this study. The research presented herein could serve as a basis for future work. Our recently developed classification and the use case presented here serve as examples. Data retrieved from, for example, other type of electronic health records and use contexts could refine and validate the suggested methodological approach.
Identification of QTLs for Grain Protein Content in Russian Spring Wheat Varieties

Most modern breeding programs aim to develop wheat (T. aestivum L.) varieties with a high grain protein content (GPC) due to its greater milling and cooking quality, and improved grain price. Here, we used a genome-wide association study (GWAS) to map single nucleotide polymorphisms (SNPs) associated with GPC in 93 spring bread wheat varieties developed by eight Russian Breeding Centers. The varieties were evaluated for GPC, grain weight per spike (GWS), and thousand-kernel weight (TKW) at six environments, and genotyped with 9351 polymorphic SNPs and two SNPs associated with the NAM-A1 gene. GPC varied from 9.8 to 20.0%, depending on the genotype and environment. Nearly 52% of the genotypes had a GPC > 14.5%, which is the threshold value for entry into high-class wheat varieties. Broad-sense heritability for GPC was moderate (0.42), which is due to the significant effect of environment and genotype × environment interactions. GWAS performed on mean GPC evaluated across six environments identified eleven significant marker-trait associations, of which nine were physically mapped on chromosome 6A. Screening of wheat varieties for allelic variants of the NAM-A1 gene indicated that 60% of the varieties contained the NAM-A1c allele, followed by 33% for NAM-A1d, and 5% for NAM-A1a alleles. Varieties with the NAM-A1d allele showed significantly (p < 0.01) smaller GPC than those with NAM-A1c and NAM-A1a. However, no significant differences between NAM-A1 alleles were observed for both GWS and TKW.
Deep Multi-Feature Transfer Network for Fourier Ptychographic Microscopy Imaging Reconstruction

Fourier ptychographic microscopy (FPM) is a potential imaging technique, which is used to achieve wide field-of-view (FOV), high-resolution and quantitative phase information. The LED array is used to irradiate the samples from different angles to obtain the corresponding low-resolution intensity images. However, the performance of reconstruction still suffers from noise and image data redundancy, which needs to be considered. In this paper, we present a novel Fourier ptychographic microscopy imaging reconstruction method based on a deep multi-feature transfer network, which can achieve good anti-noise performance and realize high-resolution reconstruction with reduced image data. First, in this paper, the image features are deeply extracted through transfer learning ResNet50, Xception and DenseNet121 networks, and utilize the complementarity of deep multiple features and adopt cascaded feature fusion strategy for channel merging to improve the quality of image reconstruction; then the pre-upsampling is used to reconstruct the network to improve the texture details of the high-resolution reconstructed image. We validate the performance of the reported method via both simulation and experiment. The model has good robustness to noise and blurred images. Better reconstruction results are obtained under the conditions of short time and low resolution. We hope that the end-to-end mapping method of neural network can provide a neural-network perspective to solve the FPM reconstruction.
The Effect of Emotional Intelligence Training on Self-Efficacy in Nursing Students

Students who are in the last semester of their degree often experience stress because of their academic tasks. Stress that cannot be controlled can have a negative impact, especially emotionally, which can result in a decrease in self-efficacy. The objective of this research was to identify the effect of emotional intelligence training on self-efficacy in nursing students. This research was quasi-experimental (pre-test post-test with a control group), and 60 undergraduate nursing students at level 3 were recruited using purposive sampling. The measuring instrument used was the General Self Efficacy questionnaire. The results showed that the majority of respondents in this study were women (81.7%) with an average age of 20-21 years. The paired t test showed that in the intervention group there was a significant difference in the average values of the pre-test and post-test (p < 0.001), whereas in the control group there was no significant different (p = 0.549). The ANCOVA test results produced a p-value < 0.001, showing that emotional intelligence training led to increased self-efficacy in nursing students at STIKep PPNI West Java. It is hoped that with this research, the campus can conduct an assessment of the level of student self-efficacy on a regular basis, so that self-efficacy in students can always be monitored. Keywords: Emotional Intelligence Training, Self-Efficacy, nursing, students
Superhydrophobic Aluminium Surface to Enhance Corrosion Resistance and Obtain Self-Cleaning and Anti-Icing Ability

A facile environmentally acceptable surface roughening method using chemical etching in HCl/H2O2 followed by grafting with n-octyltrimethoxysilane (AS-8) and 1H,1H,2H,2H-perfluorooctyltrimethoxysilane (FAS-8) was studied to fabricate a (super)hydrophobic aluminium surface. The ground aluminium surface after selected etching times (before and after grafting), was characterised using a contact profilometer, optical tensiometer, scanning electron microscope coupled with an energy-dispersive spectroscope and X-ray photoelectron spectroscope to evaluate surface roughness, wettability, surface morphology and composition. The durability of the grafted surface was tested using thermal and UV resistance tests. The corrosion properties were evaluated using potentiodynamic measurements and standard salts spray testing, ASTM B117-19. Finally, the self-cleaning and anti-icing abilities were assessed. The grafted aluminium surface with octyl- or perfluorooctyl silane reflected the highly hydrophobic (AS-8) and superhydrophobic behaviour (FAS-8). Moreover, the different behaviour of the octyl- or perfluorooctyl chain in the silane molecule on modified surface properties was also noticed because durability tests confirmed greater thermal, UV stability and corrosion resistance of FAS-8 compared to AS-8. The aluminium etched for 2 min and grafted with FAS-8 also demonstrated an excellent self-cleaning and anti-icing performance.
Oral Administration of Water Extract from Euglena gracilis Alters the Intestinal Microbiota and Prevents Lung Carcinoma Growth in Mice

The antitumor effects of a partially purified water extract from Euglena gracilis (EWE) and EWE treated by boiling (bEWE) were evaluated using orthotopic lung cancer syngeneic mouse models with Lewis lung carcinoma (LLC) cells. Daily oral administration of either EWE or bEWE started three weeks prior to the inoculation of LLC cells significantly attenuated tumor growth as compared to the phosphate buffered saline (PBS) control, and the attenuation was further enhanced by bEWE. The intestinal microbiota compositions in both extract-treated groups were more diverse than that in the PBS group. Particularly, a decrease in the ratio of Firmicutes to Bacteroidetes and significant increases in Akkermansia and Muribaculum were observed in two types of EWE-treated groups. Fecal microbiota transplantation (FMT) using bEWE-treated mouse feces attenuated tumor growth to an extent equivalent to bEWE treatment, while tumor growth attenuation by bEWE was abolished by treatment with an antibiotic cocktail. These studies strongly suggest that daily oral administration of partially purified water extracts from Euglena gracilis attenuates lung carcinoma growth via the alteration of the intestinal microbiota.
The influence of host genotypes on the endophytes in the leaf scar tissues of apple trees and correlation of the endophytes with apple canker (Neonectria ditissima) development

Bacterial and fungal endophytes may help their host in terms of improved tolerance to abiotic and biotic stresses and enhanced growth. European apple canker, caused by Neonectria ditissima, is widespread in apple growing regions. Infection by N. ditissima occurs through artificial or natural wounds, including leaf scars, picking wounds, and pruning cuts. Using F1 progeny trees in an experimental orchard derived from a cross between a canker susceptible genotype and a canker tolerant/resistant genotype, we assessed the influence of genotype of both bacterial and fungal endophyte communities in apple leaf scars, and determined correlations of endophytes with canker development. All trees were artificially inoculated with a N. ditissima isolate post-planting. Specific components of apple endophytes as well as a number of individual fungal/bacterial groups in leaf scars were partially genetically controlled by host genotypes. Several bacterial groups were significantly correlated with canker-related traits, mostly positively associated with canker tolerance. A few fungal groups may facilitate canker development whereas others may compete with canker. However, most of these microbial groups could not be identified to the species level with confidence; even for those groups which could be assigned to the species level there is insufficient knowledge about their ecological characteristics in relation to plants. The present results may be used to inform further research using biocontrol to manage N. ditissima and breeding for resistance.
Niche Theory Analysis of Sustainable Strategic Relationships among MICE Destinations: A Case of Four Cities in Waikato and Bay of Plenty Regions of New Zealand

This study aimed to analyze the competitive relationship among meetings, incentive, convention, and exhibition (MICE) destinations with reference to the notion of niche businesses in New Zealand, and to explore the existence of cooperative strategies between neighboring destinations. The data were collected from the Ministry of Business, Innovation, and Employment Reports on the Convention Activity Survey (CAS) in New Zealand. The study contributes towards an innovative and better understanding of the dynamics of sustainable destination competitiveness. It broadens the scope of MICE industry research by exploring new insights on the notion of destination competition and makes a theoretical connection between niche theory and the importance of coopetition. By investigating the case of MICE destinations of four central cities in New Zealand, this study provides information on the strategic significance of niche marketing for global destinations preparing for entrance into this market.
On Mathematical Model of Biological Pest Control of Sorghum Production

In this paper, mathematical model of Sorghum production from seed/planting to harvesting stage was presented with respect to its pests(prey) and the corresponding natural prey enemy (predators) at every stage. The model was characterized, the existence and uniqueness of the model solution was established. And finally numerical applications was carried out using Differential Transform method, and it was found that pests at different stages of sorghum production can be minimized below injury level using biological control which in turn leads to maximization of the sorghum production.
Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot

Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and the evaluation results are greatly influenced by doctors. In this study, a desktop upper limb rehabilitation robot was designed and a quantitative evaluation system of upper limb motor function for stroke patients was proposed. The kinematics and dynamics data of stroke patients during active training were collected by sensors. Combined with the scores of patients’ upper limb motor function by rehabilitation doctors using the Wolf Motor Function Test (WMFT) scale, three different quantitative evaluation models of upper limb motor function based on Back Propagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were established. To verify the effectiveness of the quantitative evaluation system, 10 healthy subjects and 21 stroke patients were recruited for experiments. The experimental results show that the BPNN model has the best evaluation performance among the three quantitative evaluation models. The scoring accuracy of the BPNN model reached up to 87.1%. Moreover, there was a significant correlation between the models′ scores and the doctors′ scores. The proposed system can help doctors to quantitatively evaluate the upper limb motor function of stroke patients and accurately master the rehabilitation progress of patients.
Research on theory and method of refinement clearing of urban rail transit passenger flow

Under the condition of urban rail transit network, accurate analysis of the temporal and spatial distribution of passenger flow is crucial for the operation management department to improve operation efficiency and service quality. Therefore, this paper studies the model and algorithm of the refined clearing process of rail transit passenger flow. Firstly, with the minimum generalized travel cost of passengers as the objective function, a refined passenger flow clearing model is constructed; secondly, a refined passenger flow clearing algorithm is designed. This research determine the effective travel path of passengers based on the improved K short path algorithm, combine the Logit model to determine the travel path of passengers, and then calculate the passenger flow indicators such as station level and section level, and calculate based on the information of the in and out station name and time contained in the AFC data the specific train serial number that passengers take; finally, verify the model and algorithm with the AFC data of the Beijing rail transit network. This research has played a good role in evaluating route transportation capacity, inducing passenger travel choices, and statistically analyzing operational information for operation managers, and provides important data support for their decision-making.
Parameter tuning of active disturbance-rejection controller for induction motor based on time scale

Active disturbance-rejection controller(ADRC) has been successfully used in induction motor(IM) control system.But ADRC parameters are difficult to be tuned.To research ADRC parameters tuning,the time scale of IM is deduced by theoretic analysis.Combining the relations between scale time and ADRC parameters,we proposed a new ADRC parameter-tuning method for IM control system,which is based on the oriented vector control of stator flux.Finally,a digital simulation study is carried out and simulation results show the proposed method makes ADRC parameter tuning easier and more effective in IM control system,especially for series motors parameters tuning.
Sequence Embeddings Help Detect Insurance Fraud

Roughly 10 percent of the insurance industry’s incurred losses are estimated to stem from fraudulent claims. One solution is to use tabular data to construct models that can distinguish between claims that are legitimate and those that are fraudulent. However, while canonical tabular data models enable robust fraud detection, complex sequential data have been out of the insurance industry’s scope. For health insurance, we propose deep learning architectures that process insurance data consisting of sequential records of patient visits and characteristics. Both the sequential and tabular components improve the quality of the model, generating new insights into the detection of health insurance fraud. Empirical results derived using relevant data from a health insurance company show that our approach outperforms state-of-the-art models and can substantially improve the claims management process. We obtain a ROC AUC metric of 0.873, while the best competitor based on state-of-the-art models achieves 0:815. Moreover, we demonstrate that our architectures are more robust to data corruption. As more and more semi-structured event sequence data become available to insurers, our methods will be valuable for many similar applications, particularly when variables have a large number of categories, such as those from the International Classification of Disease (ICD) codes or other classification codes.
Children’s agency when experiencing family-related adversities: The negotiation of closeness and distance in children’s personal narratives

Children’s perspectives on family-related adversities are important to social work practice and theory. Qualitative inquiry into children’s personal narratives can contribute to a deeper understanding of their project of handling difficult relationships and experiences in the family context. The present study explores children’s agency with a specific focus on how they negotiate difficult family-related relationships and experiences in, and through, their personal narratives. The analysis builds on teller-focused interviews with 22 children aged between 6 and 17 who receive support linked to their experiences of family-related adversities. The findings suggest that children negotiate difficult relationships and experiences in terms of closeness and distance. This is shown in their narrative practice of positioning and repositioning themselves and others in, and through, their telling. The findings are theorized in relation to the concepts of power and misrecognition. Finally, the implications for social work practice and research are discussed with an emphasis on how to promote a non-instrumental attitude to children’s perspectives and experiences within the child welfare system. The present study suggests that this pursuit would benefit from a child-centered and narrative understanding of children and their knowledge.
Mathematical Model and Simulation of Torpedo Weapon System Effectiveness Evaluation

The tactical and technical indexes of the torpedo weapon system are important factors on evaluating their effectiveness.The reliability and maintainability of torpedo weapon systems were researched to calculate the availability matrix.Dependability matrix model and capability matrix model were established by the theory of stochastic process.Finally,the torpedo weapon system effectiveness models were established and simulated using a computer.
Towards PageRank Update in a Streaming Graph by Incremental Random Walk

As the internet and the Internet of Things (IoT) have been widely applied in many application fields, a large number of graphs are continuously produced and change over time, which leads to difficulties in graph analysis and utilization. This paper studies a PageRank update algorithm for a streaming graph using incremental random walk. We focus on the information about the local changes of nodes and edges in the current graph, analyze the impact of such local changes on this current graph, and then use the idea behind wave propagation theory to seek and determine all affected nodes that need to be updated their PageRank in the new graph. For new nodes, the existing nodes in the current graph that are connected with these new nodes are aggregated into a supernode, and the PageRank of the new nodes is solved in the new graph with the supernode. Finally, we conduct a series of experiments on real-world and synthetic graph datasets. Compared with the state-of-the-art incremental computing algorithm, our algorithm not only ensures the accuracy of calculating the PageRank in a large streaming graph, but also speeds up the computational process by avoiding many redundant computations.
Dynamic Contraflow with Orientation Dependent Transit Times Allowing Intermediate Storage

Contraflow is one of the best and widely accepted techniques in evacuation planning problems, where the reversal of arcs is made to increase the amount and decrease the time of ow transmission. At the time of evacuation, if the flow value leaving the source node exceeds the bottleneck capacity of the network, then the storage of excess flow at comparatively safer intermediate nodes can be a milestone to save the life of evacuees. In this paper, we introduce the maximum dynamic contraflow problem in a general network and earliest arrival contraflow problem in two-terminal series-parallel network with asymmetric transit times on anti-parallel arcs. We present polynomial time solution strategies with orientation dependent transit times by allowing the storage of flow at intermediate nodes.