bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of a shorter algorithm in an automated analysis of sublingual microcirculation
Authors: Bruno Raphael Romano , Schemmelmann Mara , Wollborn Jakob , Kelm Malte , Jung Christian
Published: September 2020

OBJECTIVE: Diagnostic and risk stratification in intensive and emergency medicine must be fast, accurate, and reliable. The assessment of sublingual microcirculation is a promising tool for this purpose. However, its value is limited because the measurement is time-consuming in unstable patients. This proof-of-concept validation study examines the non-inferiority of a reduced frame rate in image acquisition regarding quality, measurement results, and time. METHODS: This prospective observational study included healthy volunteers. Sublingual measurement of microcirculation was performed using a sidestream dark field camera (SDF, MicroVision Medical®). Video-quality was evaluated with a modified MIQS (microcirculation image quality score). AVA 4.3C software calculated microcirculatory parameters. RESULTS: Thirty-one volunteers were included. There was no impact of the frame rate on the time needed by the software algorithm to measure one video (4.5 ± 0.5 minutes) for AVA 4.3C. 86 frames per video provided non inferior video quality (MIQS 1.8 ± 0.7 for 86 frames versus MIQS 2.2 ± 0.6 for 215 frames, p < 0.05), equal results for all microcirculatory parameters, but did not result in an advantage in terms of speed. No complications occurred. CONCLUSION: Video captures with 86 frames offer equal video quality and results for consensus parameters compared to 215 frames. However, there was no advantage regarding the time needed for the overall measurement procedure.
Effects of gut microbial metabolite trimethylamine N-oxide (TMAO) on platelets and endothelial cells
Authors: Krüger-Genge A. , Jung F. , Hufert F. , Jung E.-M. , Küpper J.-H. , Storsberg J.
Published: September 2020

Thrombotic events result from different pathologies and are the underlying causes of severe diseases like stroke or myocardial infarction. Recent basic research now revealed a link between food uptake, food conversion and gut metabolism. Gut microbial production of trimethylamine N-oxide (TMAO) from dietary nutrients like choline, lecithin and L-carnitine was associated with the development of cardiovascular diseases. Within this review we give a systematic overview about the influence of TMAO on blood components like platelets and endothelial cells which both are involved as key players in thrombotic processes. In summary, a mechanistic correlation between the gut microbiome, TMAO and cardiovascular diseases becomes obvious and emphasizes to the significance of the intestinal microbiome.
Social network service based on ABC theory
Authors: Chang Christina Ling-Hsing , Wu Sheng
Published: September 2020

BACKGROUND: The rapid development of information technology has changed the interactive behaviors in people’s daily lives. OBJECTIVE: The purpose of this paper is to investigate as to whether the characteristics of the social network service (SNS) can increase the motivation (antecedent) of the SNS users; and in turn, increase their affection and cognitive (beliefs) towards the SNS; and then increase their purchase behavior (consequence). METHODS: This study uses the antecedent belief consequence (ABC) theory, and collects data from the SNS users in Taiwan, with a total of 188 valid surveys were completed for model testing. RESULTS: The findings show that: (1) The SNS characteristics (information and service qualities), indeed, have influenced the affection and cognitive of the SNS users, but the system quality can only influence the affection of the SNS users. (2) The purchase behavior of the SNS users have been influenced by the cognitive of the SNS users only, but not by the affection. CONCLUSIONS: The result demonstrates that the beliefs of the SNS users should be considered when the researchers investigate the purchase behavior of the SNS users. This paper also discusses the contribution to the research and the SNS management practice.
A structured framework for identifying risks sources related to human resources in a 4.0 working environment perspective
Authors: Popescu Sorin , Santa Roxana , Teleaba Florian , Ilesan Hannelore
Published: September 2020

BACKGROUND: Research is focused on companies that offshored their production and operate in the international business environment, in the current globalized world placed generically under the 4.0 index, inspired by the fourth industrial revolution. OBJECTIVE: The paper aims to draw up a structured and multilayered framework for identifying sources of risks related to the work environment and to the human resources management in this context. METHODS: An extended analysis of information in the literature and on the web is conducted to define the mentioned conceptual structure and to identify domains, processes, and actions that should be considered hosting such risks. First filtered by the authors based on their experience as consultants and managers in international projects, results are then validated by successive feedbacks from their peers. RESULTS: Two levels of the framework are detailed, the first correlating globalization and 4.0 development stage challenges, identifying the risk sources within a matrix detailing on one dimension technological progress, governmental & legal issues, cultural patterns, and on the other, domains sensitive to risks regarding the human resource, such as work performance, working skills, working ethics & discipline, and working models, environment & tools. The second level focuses on the sources of risks arise due to a company’s digital transformation with regard to the choice of the working models configuration, employees’ recruitment & hiring, training, working planning, organization & control, and setting up working regulations. CONCLUSIONS: The results are intended to provide support for easier and more comprehensive identification of work and human resources related risks in the mentioned context.
Virtuality and teams: Dealing with crises and catastrophes
Authors: Bhargava Sushant
Published: September 2020

BACKGROUND: This article bridges the gap between theory and practice and elaborates, for practitioners, how to convert the COVID-19 and other similar crises into opportunities for keeping their business on track for growth. It shows how movement to virtual modes of working, especially virtual teams, can help practitioners meet the current crisis effectively and also prepare for future crisis efficiently. OBJECTIVE: The objective of this article is to show how the concept of virtuality can help design practices which enable managers/practitioners in effectively managing necessary transitions to virtual work. METHODS: The article reviews and integrates essential literature on virtuality and virtual teams. It enumerates the benefits and challenges which accompany a sudden and necessary movement to virtual work in teams. Also used are the recently developed theoretical frameworks of teams as essential emergent states and its implications on virtual work. RESULTS: By distilling insights from past literature, the article advises managers on how to deal with the present and prepare for future disruptions. Usage of overarching frameworks rather than industry/work specific literature enables managers to move away from specific recommendations and focus on general characteristics for wider impact. CONCLUSIONS: The article demonstrates how organizations can meet disruptive challenges successfully and also prepare for future challenges sustainably using virtuality as a starting point.
Vascular Risk Factors of Hippocampal Subfield Volumes in Persons without Dementia: The Medea 7T Study
Authors: Blom Kim , Koek Huiberdina L. , Zwartbol Maarten H.T. , Ghaznawi Rashid , Kuijf Hugo J. , Witkamp Theo D. , Hendrikse Jeroen , Biessels Geert Jan , Geerlings Mirjam I. ,   on behalf of the UCC-SMART Study Group
Published: September 2020

Background:

Vascular risk factors have been associated with risk of Alzheimer’s disease (AD) and volume loss of the hippocampus, but the associations with subfields of the hippocampus are understudied. Knowing if vascular risk factors contribute to hippocampal subfield atrophy may improve our understanding of vascular contributions to neurodegenerative diseases.

Objective:

To investigate the associations between age, sex, and vascular risk factors with hippocampal subfields volumes on 7T MRI in older persons without dementia.

Methods:

From the Medea 7T study, 283 participants (67±9 years, 68% men) without dementia had 7T brain MRI and hippocampal subfield segmentation. Subfields were automatically segmented on the 3D T2-weighted 7T images with ASHS software. Using linear mixed models, we estimated adjusted associations of age, sex, and vascular risk factors with z-scores of volumes of the entorhinal cortex (ERC), subiculum (SUB), Cornu Ammonis (CA)1, CA2, CA3, CA4, and dentate gyrus (DG), and tail as multivariate correlated outcomes.

Results:

Increasing age was associated with smaller volumes in all subfields, except CA4/DG. Current smoking was associated with smaller ERC and SUB volumes; moderate alcohol use with smaller CA1 and CA4/DG, obesity with smaller volumes of ERC, SUB, CA2, CA3, and tail; and diabetes mellitus with smaller SUB volume. Sex, former smoking, and hypertension were not associated with subfield volumes. When formally tested, no risk factor affected the subfield volumes differentially.

Conclusion:

Several vascular risk factors were associated with smaller volumes of specific hippocampal subfields. However, no statistical evidence was found that subfields were differentially affected by these risk factors.
Structural Change of Gut Microbiota in Patients with Post-Stroke Comorbid Cognitive Impairment and Depression and Its Correlation with Clinical Features
Authors: Ling Yi , Gu Qilu , Zhang Junmei , Gong Tianyu , Weng Xiongpeng , Liu Jiaming , Sun Jing
Published: September 2020

Background: Post-stroke comorbid cognitive impairment and depression (PSCCID) is a severe neuropsychiatric complication after acute stroke. Gut microbiota dysbiosis is associated with many psychiatric disorders. Alterations in the composition of gut microbiota may serve as a critical role in patients with PSCCID. Objective: We aimed to characterize the microbial profiles of patients with PSCCID. Method: A total of 175 stroke patients were recruited in the study. The composition of gut bacterial communities of patients was determined by 16S ribosomal RNA Miseq sequencing, and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States was used to demonstrate the functional alterations of gut microbiota. We further identified the characteristic gut microbiota of PSCCID using linear discriminant analysis effect size. Results: Patients with PSCCID exhibited an increased abundance of Proteobacteria, including Gammaproteobacteria, Enterobacteriales, and Enterobacteriaceae, and a decreased abundance of several short-chain fatty acids-producing bacteria compared with non-PSCCID patients. The abundance of Gammaproteobacteria and Enterobacteriaceae showed negative correlations with the MoCA score. Moreover, the Kyoto Encyclopedia of Genes and Genomes results demonstrated the enriched orthologs of glycan biosynthesis and metabolism and decreased orthologs of amino acid metabolism in PSCCID patients. Importantly, the characteristic gut microbiota was identified and achieved an area under the curve of 0.847 between the two groups. Conclusion: In this study, we characterized the gut microbiota of PSCCID patients, and revealed the correlations of the altered gut microbiota with clinical parameters, which took a further step towards non-invasive diagnostic biomarkers for PSCCID from fecal samples.
Stimulation Effects and Mechanisms of Different Adjuvants on a Norovirus P Particle-Based Active Amyloid-β Vaccine
Authors: Dai MingRui , Feng XueJian , Mo ZengShuo , Sun Yao , Fu Lu , Zhang Yong , Wu Jiaxin , Yu Bin , Zhang Haihong , Yu Xianghui , Wu Hui , Kong Wei
Published: September 2020

Background: Adjuvants are important components of vaccines and effectively enhance the immune response of specific antigens. However, the role of adjuvants or combinations of adjuvants in stimulating immunogenicity of the amyloid-β (Aβ) vaccine, as well as molecular mechanisms underlying such stimulation still remain unclear. A previous study of ours developed a norovirus P particle-based active Aβ epitope vaccine, PP-3copy-Aβ1-6-loop123, which stimulates a high titer of Aβ-specific antibodies in mouse Alzheimer’s disease (AD) models. Objective: The most effective and safe adjuvant that maximizes the immunogenicity of our protein vaccine was determined. Methods: We investigated four adjuvants (CpG, AS02, AS03, and MF59), and combinations of those, for capacity to enhance immunogenicity, and performed transcriptome analysis to explore mechanisms underlying the role of these in AD immunotherapy. Results: Addition of the adjuvant, AS02, remarkably improved the immunogenicity of the PP-3copy-Aβ1-6-loop123 vaccine without triggering an Aβ-specific T-cell response. Combinations of adjuvants, particularly CpG +  AS02 and CpG + AS03, elicited a significantly elevated and prolonged Aβ-specific antibody response. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that a combination of two adjuvants was more effective in activating immune-related pathways, thereby enhancing the immunogenicity of PP-3copy-Aβ1-6-loop123. Conclusion: These findings demonstrated that adjuvants can be used as enhancers in AD protein vaccination, and that a combination of CpG and AS-related adjuvants may be a very effective adjuvant candidate suitable for further clinical trials of the PP-3copy-Aβ1-6-loop123 vaccine. Our studies also revealed potential mechanisms underlying the stimulation of immune response of protein vaccines by adjuvants.
Neuronal Calcium Imaging, Excitability, and Plasticity Changes in the Aldh2–/– Mouse Model of Sporadic Alzheimer’s Disease
Authors: Ghoweri Adam O. , Gagolewicz Peter , Frazier Hilaree N. , Gant John C. , Andrew R. David , Bennett Brian M. , Thibault Olivier
Published: September 2020

Background: Dysregulated signaling in neurons and astrocytes participates in pathophysiological alterations seen in the Alzheimer’s disease brain, including increases in amyloid-β, hyperphosphorylated tau, inflammation, calcium dysregulation, and oxidative stress. These are often noted prior to the development of behavioral, cognitive, and non-cognitive deficits. However, the extent to which these pathological changes function together or independently is unclear. Objective: Little is known about the temporal relationship between calcium dysregulation and oxidative stress, as some reports suggest that dysregulated calcium promotes increased formation of reactive oxygen species, while others support the opposite. Prior work has quantified several key outcome measures associated with oxidative stress in aldehyde dehydrogenase 2 knockout (Aldh2–/–) mice, a non-transgenic model of sporadic Alzheimer’s disease. Methods: Here, we tested the hypothesis that early oxidative stress can promote calcium dysregulation across aging by measuring calcium-dependent processes using electrophysiological and imaging methods and focusing on the afterhyperpolarization (AHP), synaptic activation, somatic calcium, and long-term potentiation in the Aldh2–/– mouse. Results: Our results show a significant age-related decrease in the AHP along with an increase in the slow AHP amplitude in Aldh2–/– animals. Measures of synaptic excitability were unaltered, although significant reductions in long-term potentiation maintenance were noted in the Aldh2–/– animals compared to wild-type. Conclusion: With so few changes in calcium and calcium-dependent processes in an animal model that shows significant increases in HNE adducts, Aβ, p-tau, and activated caspases across age, the current findings do not support a direct link between neuronal calcium dysregulation and uncontrolled oxidative stress.
S-Adenosylmethionine Rescues Cognitive Deficits in the rTg4510 Animal Model by Stabilizing Protein Phosphatase 2A and Reducing Phosphorylated Tau
Authors: Beauchamp Leah C. , Liu Xiang M. , Sedjahtera Amelia , Bogeski Mirjana , Vella Laura J. , Bush Ashley I. , Adlard Paul A. , Barnham Kevin J.
Published: September 2020

Background: Alterations in the methionine cycle and abnormal tau phosphorylation are implicated in many neurodegenerative diseases, including Alzheimer’s disease and frontotemporal dementia. rTg4510 mice express mutant human P301L tau and are a model of tau hyperphosphorylation. The cognitive deficit seen in these animals correlates with a burden of hyperphosphorylated tau and is a model to test therapies aimed at lowering phosphorylated tau. Objective: This study aimed to increase protein phosphatase 2A activity through supplementation of S-adenosylmethionine and analyze the effect on spatial memory and tau in treated animals. Methods: 6-month-old rTg4510 mice were treated with 100 mg/kg S-adenosylmethionine by oral gavage for 3 weeks. Spatial recognition memory was tested in the Y-maze. Alterations to phosphorylated tau and protein phosphatase 2A were explored using immunohistochemistry, western blot, and enzyme-linked immunosorbent assays. Results: Treatment with S-adenosylmethionine increased the Y-maze novel arm exploration time and increased both the expression and activity of protein phosphatase 2A. Furthermore, treatment reduced the number of AT8 positive neurons and reduced the expression of phosphorylated tau (Ser202/Thr205). S-adenosylmethionine contributes to multiple pathways in neuronal homeostasis and neurodegeneration. Conclusion: This study shows that supplementation with S-adenosylmethionine stabilizes the heterotrimeric form of PP2A resulting in an increase the enzymatic activity, a reduced level of pathological tau, and improved cognition.
Wireless power transmission with a sown auxiliary coil to improve power efficiency for an implantable device
Authors: Sasaki Ryunosuke , Koizumi Kohei , Kiryu Shogo
Published: September 2020

In this study, a magnetic resonance wireless power transmission system with a sown auxiliary coil is described. The system is proposed to deliver power to an “implantable cardioverter defibrillator” (ICD) device from a bed. The auxiliary coil is supposed to be sown in a shirt. With the coil, the poor coupling coefficient and the effective quality factor can be improved. In this study, the power efficiency was investigated on a magnetic resonance wireless power transmission system with a sown auxiliary coil by using a 1/2 scaled model. As a result, when transfer distance was 100 mm (200 mm in the full scaled model) and lord resistance was 20 Ω, the maximum transfer efficiency with an auxiliary coil was measured to be about 70% much higher than without that.
The effect of DC magnetic field on signal characteristics of magnetic Barkhausen noise
Authors: Liu Chengyong , Li Erlong , Wang Shiqiang , Wu Jianbo , Fang Hui , He Sha
Published: September 2020

Magnetic Barkhausen Noise (MBN) detection technology shows great potential in the pipeline detection of stress, microstructure, and fatigue damage. However, in actual application, some pipes such as those near power plant are exposed to external magnetic fields or contain remanence. The presence of these magnetic fields will affect MBN features and then influence the testing results. To investigate the effect of DC magnetic field on MBN signal characteristics, theoretical analysis and experimental verification were carried out in this paper. It is found that the intensity and direction of the DC magnetic field have great effects on MBN signal characteristics, including the changes in the energy of MBN signal, the peak position and the shape of MBN signal profile.
Wye-delta winding configuration for brushless operation of a wound field synchronous machine
Authors: Ayub Muhammad , Hussain Asif , Sirewal Ghulam Jawad , Kwon Byung-il
Published: September 2020

This manuscript proposes a wye-delta winding configuration for brushless operation of a wound field synchronous machine (BL-WFSM). In existing third harmonically excited BL-WFSM topologies, the additional third harmonic component of the stator magnetomotive force (MMF) was produced by an extra thyristor drive circuit or by utilizing an additional inverter. In the proposed single inverter topology, the stator winding is divided into two coil sets: the wye coil and delta coil. Triplen harmonic currents are produced owing to the delta coils set and start to circulate in the delta coils. This triplen harmonic circulating currents produce a triplen harmonic pulsating MMF in the airgap. The additional triplen harmonic pulsating MMF induces voltage in the rotor harmonic winding. This induced voltage in the harmonic winding is used to excite the rotor field. A 2D finite element analysis is performed, and the simulation results confirmed the operation principle of the proposed brushless topology.
Higher education in Iran: An investigation of its expansion during 2005–2015 period using a control function approach
Authors: Mirzaei Abbasabadi Hamed , Ghaemi Asl Mahdi
Published: September 2020

This article aims at investigating the significant higher education expansion in the Islamic Republic of Iran during 2005–2015 period through employing the production function of higher education. Avoiding simultaneity and selection problems in the presence of shocks, we have used a novel method from industrial organization discipline introduced by Rovigatti and Mollisi [ 1 ] – which is officially offered embeded in a Stata[Formula: see text] module – by providing different production function estimators (Olley-Pakes, Levinsohn-Petrin, and Wooldridge) using provincial data on Iran. Our empirical results reveal that physical capital is the most critical determinant of higher education graduates. The results also uncover some important facts about the contribution of academic staff to the process of graduation. Compared to other conventional estimation methods, we also provide evidence on the superiority of this innovative method, which is far beyond its original context.
Complex evolutionary artificial intelligence in cognitive digital twinning
Authors: Wagner Neal , Sundhararajan  , Son Le Hoang , Joo Meng
Published: September 2020

This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of complex evolutionary artificial intelligence in cognitive digital twinning.
Integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network
Authors: Wang Yijie , Fu Peihua
Published: September 2020

With the enhancement of people’s environmental awareness, improving environmental performance has become an important way for manufacturing enterprises to achieve sustainable development. At present, one of the key challenges facing enterprises in terms of environmental sustainability is to extend it to other supply chain members. In this paper, the author analyzes the integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network. Supply chain integration represents the company’s ability to formulate strategic alliances, integrate resources, establish seamless processes and share information. The empirical results show that it is feasible to evaluate the performance of green logistics enterprise integration. Through reasonable calculation method and model, the effective and reasonable evaluation results can be obtained. Enterprise managers can reasonably evaluate the key links of enterprise management, allocate enterprise resources correctly, completely and reasonably, minimize costs and maximize profits. Therefore, for the green logistics enterprises, should pay attention to the enterprise’s own performance evaluation, timely adjust their own development direction, plan and goal.
Research on athlete’s action recognition based on acceleration sensor and deep learning
Authors: Geng Xiao
Published: September 2020

Due to the difficulty of athletes’ motion recognition, there are few studies on athletes’ specific motion recognition. Based on this, this study uses the acceleration sensor as the carrier, and uses human-computer interaction to transform the action of the athlete into a machine-identifiable action unit. At the same time, this paper combines the actual situation of human body motion to construct a human body motion model and builds a corresponding computer hardware and software platform. Moreover, this paper designs a classification recognition algorithm that can recognize the movement of athletes and builds SVM model based on machine learning for classification and recognition. In addition, in this study, the effectiveness of the algorithm was studied through experimental comparison. Finally, the simulation analysis was carried out to obtain the corresponding research results, and the results were analyzed by combing statistics. The research shows that the proposed algorithm can classify and recognize the collected motion data, and it has certain effects on the theoretical analysis of athletes’ motion recognition. Moreover, the algorithm can perform motion quality analysis and provide theoretical reference for subsequent related research.
SVM-based global vision system of sports competition and action recognition
Authors: Sun Changxin , Ma Di
Published: September 2020

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.
Human resource management model based on three-layer BP neural network and machine learning
Authors: Wei Guohua , Jin Yi
Published: September 2020

At present, data is in a state of explosive growth. The rapid growth of data collected by enterprises has exceeded the processing capacity of traditional human resource management systems, resulting in their inability to perform data management and data analysis. In order to improve the practicality of the human resource management system, this paper applies machine learning technology to the human resource management system, selects dimensions according to the prediction method, and builds a combined model consisting of an optimized GM (1,1) model and a BP neural network model. The model is implemented by a three-layer BP neural network. In order to verify the performance of the research model, this article conducts research using an entity as an example. The research results show that the method proposed in this paper has certain practical effects and can improve the reference for subsequent related research.
Risk spillover in financial markets based on support vector quantile regression
Authors: Xie Wangsong
Published: September 2020

In terms of financial market risk research, with the rapid popularization of non-linear perspectives and the improvement of theoretical reasoning, scholars have slowly broken through the cage of linear ideas and derived new and more practical methods from non-linear perspectives to make up for the shortcomings of traditional research. Based on the support vector classification regression algorithm, this research combines the typical facts and characteristics of financial markets, from the perspective of quantile regression and SVR intelligent technology in computer science, to explore the research method of financial market risk spillover effects from a nonlinear perspective. Moreover, this research integrates statistical research, machine learning and other related research methods, and applies them to the measurement of financial risk spillover effects. The empirical analysis shows that the method proposed in this paper has certain effects, and financial risk analysis can be performed based on the risk spillover effect measurement model constructed in this paper.
Simulation of English speech emotion recognition based on transfer learning and CNN neural network
Authors: Chen Xuehua
Published: September 2020

The difference between English and Chinese expressions is that English emphasizes the stress of syllables, so the recognition of English speech emotions plays an important role in learning English. This study uses transfer learning as the technical support to study English speech emotion recognition. The acoustic model based on weight transfer has two different training strategies: single-stage training and two-stage training strategy. By comparing the performance of the English speech emotion recognition model based on CNN neural network and the model proposed in this paper, the statistical comparison data is drawn into a statistical graph. The research results show that transfer learning has certain advantages over other algorithms in English speech emotion recognition. In the subsequent teaching and real-time translation equipment research, transfer learning can be applied to English models.
Simulation of English part-of-speech recognition based on machine learning prediction algorithm
Authors: Lanlan Pan , Liangyu Hu , Zhengya Li
Published: September 2020

English part-of-speech intelligent recognition is the scientific and technological basis for the development of intelligent speech systems. The difficulty in the current English speech recognition system lies in the recognition of English parts of speech. In order to improve the effect of English part-of-speech recognition, this study builds the language rules and morphological models of English morphological forms based on machine learning algorithms. Moreover, this study proposes a stemming extraction algorithm and a syllable division algorithm based on English characteristic rules. By studying basic phrases in English, this study analyzes the compositional structure of phrases, and determines the basic phrase structure and composition rules of English such as noun, verb, and adjective. In addition, this research studies the basic English phrase recognition algorithm based on the rule method and the analysis of basic phrase ambiguity resolution. Finally, this study designs a control experiment to analyze the performance of the algorithm proposed in this paper model and confirm the classification algorithm. The research results show that the algorithm proposed in this paper has a certain practical effect.
Learning information recommendation based on text vector model and support vector machine
Authors: Lin Liu
Published: September 2020

The difficulty of knowledge point recommendation based on the learning diagnosis model lies in how to perform feature recognition and selection of recommended knowledge points. At present, the recommendation system has certain problems in the accuracy of recommended knowledge points. Based on this, this study mainly studies the personalized problem recommendation of middle school students in the field of education. Moreover, this study takes the answer records of students’ exercises as data, and combines the characteristics of the field of education to propose an exercise recommendation algorithm based on hidden knowledge points and an exercise recommendation method based on the decomposition of student exercise weight matrix. In addition, in order to verify the effectiveness of this research algorithm, this paper selects the accuracy rate and recall rate as evaluation indicators to analyze the recommendation results of this algorithm and the current more advanced CF algorithm, and the statistical experiment results are drawn into charts. The research results show that the method proposed in this paper has certain advantages and can be used as one of the subsystems of the learning system.
Evaluation model of classroom teaching quality based on improved RVM algorithm and knowledge recommendation
Authors: Qianna Sun
Published: September 2020

The intelligent evaluation of classroom teaching quality is one of the development directions of modern education. At present, some teaching quality evaluation models have accuracy problems, and the evaluation process is affected by a variety of interference factors, which leads to inaccurate model results, and it is impossible to find out the specific factors that affect teaching. In order to improve the accuracy of classroom teaching quality evaluation, this study improves RVM based on the method of feature extraction and empirical modal decomposition of ACLLMD method, and establishes classroom theoretical teaching quality evaluation model and experimental teaching quality evaluation model based on RVM algorithm. Moreover, this study uses test data to analyze the accuracy and reliability of the evaluation results to verify the feasibility and reliability of the new method. In addition, this study verifies the reliability of this algorithm by comparing with the manual scoring results. The research results show that RVM can be used to construct classroom theory teaching quality evaluation models and experimental teaching quality evaluation models with high accuracy and good reliability.
Research on innovation and entrepreneurship based on artificial intelligence system and neural network algorithm
Authors: Chen Haixia
Published: September 2020

Innovation and entrepreneurship are an important support for social and economic development in the new era, and it is also the key to the cultivation of practical talents in universities. In order to mine the effective information of innovation and entrepreneurship data, based on the neural network algorithm, this paper combines the bat algorithm to construct a data processing model to obtain an artificial intelligence innovation and entrepreneurship system with data analysis capabilities. Moreover, this study combines with actual needs to improve the algorithm, effectively eliminate the noise existing in the data, eliminate the interference of invalid data on the judgment ability of the system model, and choose the best denoising algorithm through comparison and verification of various algorithms. In order to verify the model proposed in this paper, the data is input into this research model by collecting data in a college survey, so as to verify and analyze the performance of the model. The research results show that the artificial intelligence system proposed in this paper has good performance and has certain practical value.
Error minimization of positive output luo converter using various optimization techniques
Authors: Renjini G.S. , Deepa T.
Published: September 2020

DC-DC converters are widely used in many consumer electronic devices such as computers, medical equipment, battery chargers, cellular phones and many Industrial drives. These electronic devices require different voltage levels which is supplied from battery or some external supply. In multiple battery mission voltage decays as its stored energy is drained and requires large saving space. The switched DC-DC converters overcome these drawbacks and also regulate the output voltage for different power levels efficiently. This paper elaborates the structure of Luo converter with optimized PI controller. Positive Output Elementary Luo Converter (POELC) is designed for boost operation by choosing the appropriate duty cycle. The PI controller parameters are optimized using Cuckoo and Crow search algorithms. The proposed control methods are investigated for the transient and steady state region. The sensitivity of these controllers to supply load and line disturbances are also studied along with the servo response are presented. The controller incorporates a Luo converter is evaluated in terms of Integral Time Square Error (ITSE) and Integral Time Absolute Error. Dynamic modelling of the power converter is derived by using state space averaging method. The simulation model of the Luo converter with its control circuit is implemented in MATLAB/SIMULINK. Experimental result shows that Cuckoo PI controller has significantly performance improvement in comparison with both the conventional and Crow PI controller.
Applications of virtual reality modeling language technology for COVID-19 pandemic
Authors: Weining Sun , Cheli Zhao
Published: September 2020

During COVID-19 pandemic, researchers have used innovative technologies for fast tracking the development to end this pandemic. Virtual Reality (VR) has offered an imperative role for fighting this pandemic, through audiovisual-based virtual communication. Virtual reality modeling language (VRML), as an international standard of virtual reality, has developed rapidly. VRML expanded the function of script node by introducing Java and script programs written in java script language. This paper presents a VRML method. Libraries and platoons are virtualized to meet the normal use of users. In principle, any text editing system can be used for VRML programming, but some editing systems have few related functions and are not suitable for large-scale VRML Scene Design. The VRML algorithm proposed in this paper can be applied to large buildings. The VRML algorithm proposed in this paper is compared with the traditional algorithm. The VRML algorithm proposed in this paper is superior to the traditional algorithm in the aspects of realism, interactivity, design rationality and execution speed. The practicability of the VRML algorithm is proved. It provides help for people who are inconvenient to go out during the protection period of covid-19.
Intelligent and secure RFID multilevel fuzzy inference system for client to banker profiling
Authors: Kaul Sonam Devgan , Hatzinakos Dimitrios
Published: September 2020

In this work, we will be investigating, developing and implementing an intelligent RFID system in conjunction with a fuzzy data classification system, to greatly enhance and secure financial transactions and improve operational efficiency in the banking environment. The innovative part of this research is to provide an efficient solution to the challenge that may arise from the need to expertly and automatically match the profile of customer and banker and solve the vagueness in customer/banking profiling. Our proposal offers an expert, secure, efficient and comprehensive framework, methodology and its application in financial environments to develop customer to banker profile matching and availability via an expert agent multi level fuzzy data classification system. Foremost, according to clients and banking staff members weighted attributes, exact match has been established according to highest degree of relevance by utilizing Matlab fuzzy inference system. Then, to communicate output of a match profile engine from one party to another, to show profiling effectiveness and to do implementation; secure, privacy preserving, and comprehensive intelligent RFID profiling authentication system has been designed and verified by Scyther tool.
Fuzzy multi-objective location and routing problem
Authors: Shahsavari-Pour Nasser , Bahram-Pour Najmeh , Kazemi Mojde
Published: September 2020

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.
Several types of hesitant fuzzy filters on residuated lattices
Authors: Liu Zhi Qiang , Ma Zhen Ming
Published: September 2020

The present paper investigates the hesitant fuzzy filters on residuated lattices. A one-to-one correspondence between the set of all hesitant fuzzy filters and the set of all hesitant fuzzy congruences is established and a quotient residuated lattice with respect to a hesitant fuzzy filter is induced. Furthermore, several special types of hesitant fuzzy filters such as hesitant fuzzy implicative, regular and Boolean filters are introduced, and some alternative definitions of them are obtained, then some typical logical algebras are characterized by these identity forms.
Study on performance evaluation of the production process - fuzzy MCDM approach
Authors: Zahar Djordjevic Marija , Simeunovic Barbara , Nestic Snezana , Aleksic Aleksandar , Puskaric Hrvoje
Published: September 2020

Improvement of the production process presents a very important management task for both researchers and practitioners and enables a better market position of the enterprise. Key Performance Indicators (KPIs) of the production process can provide useful information on the current state of the ongoing process. In this paper, the relative importance of KPIs and their values at the enterprise level were assessed by the experts and decision-makers. Their estimates are described by the linguistic variables which were modeled by intuitionistic fuzzy numbers. The weights vector of KPIs at the level of the considered enterprise is given by the Fuzzy Analytic Hierarchical Process (FAHP) with Triangular Intuitionistic Fuzzy Numbers (TIFNs). The rank of enterprises with respect to KPIs’ values and their weights was calculated using the modified TOPSIS with TIFNs. The developed model was tested on 30 enterprises from Serbia, belonging to the sector of small and medium-sized (SME) production enterprises. The improvement strategies of KPIs should be proposed at the level of each enterprise, separately, respecting the KPIs’ values of the first-ranked enterprise.
Modeling and empirical analysis of regional hub-and-spoke road-rail combined transport network based on uncertain cost-time-demand
Authors: Xu Junxiang , Zhang Jin , Guo Jingni
Published: September 2020

Taking into account the uncertainties of the factors of in-transit transportation cost, hub transshipment cost, hub construction cost, in-transit transportation time, hub transshipment time, and demand, this study uses triangular fuzzy numbers, expected value criteria, and distribution of credibility measure to minimise the total transportation cost of the hub-and-spoke road-rail combined transport (RRCT) network and the maximum transportation limit time between the origin and destination of the network. Firstly, a non-linear programming mathematical model is constructed for the regional hub-and-spoke RRCT network based on uncertain cost-time-demand. Then, an improved genetic algorithm is designed to obtain an optimized scheme. The algorithm uses genetic algorithm to search the global space, and uses two local search methods, i.e. shift and exchange, to search the local space. Finally, the RRCT network along the Yaan-Linzhi section of the Sichuan-Tibet Railway is used as the research object to verify the applicability and effectiveness of the regional hub-and-spoke RRCT network model and the algorithm proposed in the study.
The Canadian Neuromuscular Disease Registry 2010–2019: A Decade of Facilitating Clinical Research Througha Nationwide, Pan-NeuromuscularDisease Registry
Authors: Hodgkinson V. , Lounsberry J. , M’Dahoma S. , Russell A. , Jewett G. , Benstead T. , Brais B. , Campbell C. , Johnston W. , Lochmüller H. , McCormick A. , Nguyen C. T. , O’Ferrall E. , Oskoui M. , Abrahao A. , Briemberg H. , Bourque P.R. , Botez S. , Cashman N. , Chapman K. , Chrestian N. , Crone M. , Dobrowolski P. , Dojeiji S. , Dowling J. J. , Dupré N. , Genge A. , Gonorazky H. , Grant I. , Hasal S. , Izenberg A. , Kalra S. , Katzberg H. , Krieger C. , Leung E. , Linassi G. , Mackenzie A. , Mah J. K. , Marrero A. , Massie R. , Matte G. , McAdam L. , McMillan H. , Melanson M. , Mezei M. M. , O’Connell C. , Pfeffer G. , Phan C. , Plamondon S. , Poulin C. , Rodrigue X. , Schellenberg K. , Selby K. , Sheriko J. , Shoesmith C. , Smith R.G. , Taillon M. , Taylor S. , Venance S. , Warman-Chardon J. , Worley S. , Zinman L. , Korngut L.
Published: September 2020

We report the recruitment activities and outcomes of a multi-disease neuromuscular patient registry in Canada. The Canadian Neuromuscular Disease Registry (CNDR) registers individuals across Canada with a confirmed diagnosis of a neuromuscular disease. Diagnosis and contact information are collected across all diseases and detailed prospective data is collected for 5 specific diseases: Amyotrophic Lateral Sclerosis (ALS), Duchenne Muscular Dystrophy (DMD), Myotonic Dystrophy (DM), Limb Girdle Muscular Dystrophy (LGMD), and Spinal Muscular Atrophy (SMA). Since 2010, the CNDR has registered 4306 patients (1154 pediatric and 3148 adult) with 91 different neuromuscular diagnoses and has facilitated 125 projects (73 academic, 3 not-for-profit, 3 government, and 46 commercial) using registry data. In conclusion, the CNDR is an effective and productive pan-neuromuscular registry that has successfully facilitated a substantial number of studies over the past 10 years.