bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Anosognosia on Clinical and Patient-Reported Assessments of Psychiatric Symptoms in Huntington’s Disease
Authors: Isaacs David , Gibson Jessie S. , Stovall Jeffrey , Claassen Daniel O.
Published: September 2020

Background: Psychiatric symptoms are widely prevalent in Huntington’s disease (HD) and exert greater impact on quality of life than motor manifestations. Despite this, psychiatric symptoms are frequently underrecognized and undertreated. Lack of awareness, or anosognosia, has been observed at all stages of HD and may contribute to diminished patient self-reporting of psychiatric symptoms. Objective: We sought to evaluate the impact of anosognosia on performance of commonly used clinical rating scales for psychiatric manifestations of HD. Methods: We recruited 50 HD patients to undergo a formal psychiatrist evaluation, the Problem Behavior Assessment-Short Form (PBA-s), and validated self-report rating scales for depression, anxiety, and anger. Motor impairment, cognitive function, and total functional capacity were assessed as part of clinical exam. Patient awareness of motor, cognitive, emotional, and functional capacities was quantified using the Anosognosia Rating Scale. Convergent validity, discriminant validity, classification accuracy, and anosognosia effect was determined for each psychiatric symptom rating scale. Results: Anosognosia was identified in one-third of patients, and these patients underrated the severity of depression and anxiety when completing self-report instruments. Anosognosia did not clearly influence self-reported anger, but this result may have been confounded by the sub-optimal discriminant validity of anger rating scales. Conclusion: Anosognosia undermines reliability of self-reported depression and anxiety in HD. Self-report rating scales for depression and anxiety may have a role in screening, but results must be corroborated by provider and caregiver input when anosognosia is present. HD clinical trials utilizing patient-reported outcomes as study endpoints should routinely evaluate participants for anosognosia.
Classification accuracy of basketball simulation training system based on sensor fusion and Bayesian algorithm
Authors: Zhu Wei
Published: September 2020

As a pattern recognition application direction, human body posture recognition provides decision-making basis for human body behavior pattern analysis of human-computer intelligent interactive control. Therefore, in a complete human-computer intelligent interaction system, human body posture recognition is a necessary link that can complete the human body’s behavioral characterization and make humanized decision-making. This paper studies the athlete’s posture recognition algorithm based on multi-sensor method and completes the whole process from data acquisition to data processing and model algorithm construction and verification. Moreover, this paper designs experiments to verify the model’s recognition results for athletes, and discusses the results, and analyzes the advantages and disadvantages of the model in this experiment. In addition, this study takes basketball action as an example to take identification analysis. The results show that the proposed method has certain practical effects and can provide theoretical reference for subsequent related research.
A novel approach using modified filtering algorithm (MFA) for effective completion of cloud tasks
Authors: Jha Sudan , Prashar Deepak , Elngar Ahmed A.
Published: September 2020

In today’s era, cloud computing has played a major role in providing various services and capabilities to a number of researchers around the globe. One of the major problems we face in cloud is to identify the various constraints related with the delay in the Task accomplishment as well as the enhanced approach to execute the task with high throughput. Many studies have shown that it is almost difficult to create an ideal solution but it seems feasible to provide a sub-optimal solution utilizing heuristic algorithms. In this paper, compared to previously used particle swarm optimization (PSO), heuristic approaches, and improved PSO algorithm for efficient task scheduling, we propose “Modified Filtering Algorithm” for task scheduling on cloud setting. Comparing all these three algorithms, we strive to build an optimum schedule to reduce the completion period of execution of activities.
Conventional versus Islamic bank efficiency: A dynamic network data-envelopment-analysis approach
Authors: Azad Md Abul Kalam , Talib Muzalwana Binti Abdul , Kwek Kian-Teng , Saona Paolo
Published: September 2020

This study compares the efficiency of conventional and Islamic banks in Malaysia by engaging in a dynamic three-step (production, intermediation, and profitability) network data envelopment analysis (DEA). The inputs and outputs for the DEA model are selected based on the CAMELS rating. The major contributions of this study are threefold. First, this study investigates the efficiency of Malaysian banks using a novel dynamic network DEA model. Second, the Malaysian banking industry is found to be efficient in creating earning assets rather than in creating loans or profit. The results reveal that only a few banks in Malaysia have been efficient in converting deposits and equities into profit. Third, Islamic banks, in general, have been performing efficiently in the production and profitability approaches. Conventional banks, in contrast, are found to have been efficient in the intermediation approach. Policy implications are derived from the main conclusions.
Interactive intelligent teaching and automatic composition scoring system based on linear regression machine learning algorithm
Authors: Yuan Zhang
Published: September 2020

Intelligent education is an intelligent education platform that integrates correct education concept and Internet of things, big data, cloud computing and other technologies. This paper hopes to use the construction of the composition scoring model to further construct a computer scoring system for college English translation, which can give students a translation score and give feedback evaluation based on the quality of the translation. In this paper, according to the knowledge of the existing automatic scoring system at home and abroad, the feature selection method (TF-IDF, IG, CHI) is discussed and analyzed. Moreover, this paper studies the impact of our composition automatic scoring from the perspective of linguistics. In addition, this paper uses the multiple regression method to evaluate the final score. The features considered in this paper mainly include simple linguistic features and complex linguistic features. Finally, performance analysis of the algorithm model is performed by setting up a control experiment. The research results show that the proposed algorithm model has certain effects. The future trend is to form adult auxiliary machines through various human-computer interaction technologies, which will reshape future learning and education and form a new teaching form.
Analysis of consumer online resale behavior measurement based on machine learning and BP neural network
Authors: Zou Xinlu
Published: September 2020

The reasons for consumers’ resale behavior are complex and sometimes diverse, and the investigation of consumer resale behavior is not a simple matter. Therefore, only through a lot of investigation and inquiry can we reach relevant conclusions. Based on machine learning and BP neural network, this paper constructs a consumer online resale behavior measurement model. The contraction-expansion factor can balance the global search and local search capabilities in different iteration periods, and the differential evolution operator is introduced to solve the problem of lack of population diversity. After building the model, this study collects data through questionnaires, and combines neural network training models to take data training and data prediction. In addition, this study compares and analyzes real data with predicted data, and visually displays the comparison results through statistical graphs. The results show that the method proposed in this paper has certain effects and can provide theoretical references for subsequent related research.
Multi-feature recognition of English text based on machine learning
Authors: Qi Ao , Narengerile Liu
Published: September 2020

At present, the recognition method based on character segmentation is not effective in recognizing English text, and the traditional methods are based on the structural features and statistical characteristics of strokes. In order to improve the recognition effect of in English text, from the perspective of machine learning, this study introduces multi-features to improve the lack of information caused by the small Chinese data set. Moreover, this study disassembles the character recognition problem into a text matching problem of question and answer, and the textual entailment problem of answer and standard answer and continues training on the data set of short text score. The final result has a certain improvement, which proves the usability of the mechanism designed in this paper. In order to study the performance of the model proposed in this paper, the model proposed in this paper and the neural network recognition model are compared in terms of recognition accuracy and recognition speed. The research results show that the algorithm proposed in this paper has a certain effect.
Online teaching quality evaluation model based on support vector machine and decision tree
Authors: Hou Jingwen
Published: September 2020

At present, online education evaluation models are insufficient when dealing with small-scale evaluation data sets. In order to discriminate the learner’s learning state, this paper further studies online teaching machine learning methods, and introduces adaptive learning rate and momentum terms to improve the gradient descent method of BP neural network to improve the convergence rate of the model. Moreover, this study proposes a deep neural network model to deal with complex high-dimensional large-scale data set problems. In the process of supervised prediction, this study uses support vector regression as a predictor for supervised prediction, and this study maps complex non-linear relationships into high-dimensional space to achieve a linear relationship similar to low-dimensional space. In addition, in this study, small-scale teaching quality evaluation data sets and large-scale data sets are input into the model to perform experiments. Finally, the model proposed in this study is compared with other shallow models. The results show that the model proposed in this research is effective and advantageous in evaluating teaching quality in universities and processing large-scale data sets.
Recognition of English information and semantic features based on SVM and machine learning
Authors: Li Man , Bai Ruifang
Published: September 2020

With the deepening of people’s research on event anaphora, a large number of methods will be used in the identification and resolution of event anaphora. Although there has been some progress in the resolution of the current event, the difficult problems have not yet been completely resolved. This study analyzes the English information anaphora resolution based on SVM and machine learning algorithms and uses the CNN three-layer network as the basis to model the structure. Moreover, this study improves the semantic features by adding semantic roles and analyzes and compares the performance of the improved semantic features with those before the improvement. In addition, this study combines semantic features to compare and analyze each feature combination and uses a dual candidate model to improve the system. Finally, this study analyzes the experimental results. The results show that the performance of the system using the dual candidate model is better than that of the single candidate model system.
Simulation of athletes’ motion detection and recovery technology based on monocular vision and biomechanics
Authors: Deng Liping , Deng Yuanxiang , Bi Zhuo
Published: September 2020

Athletes’ sports detection has a heavy pressure on athletes’ training and post-injury rehabilitation. In the traditional mobilization test, there is no effective combination of exercise and rehabilitation, which directly leads to the athlete’s physical health cannot be guaranteed. Based on this, this study combines the current situation of the athletes’ field and the training ground, and uses monocular vision as the video input interface, and combines the monocular vision technology in the research. Moreover, in the research, this paper combines the human body model to construct an athlete’s human body model that adapts to monocular vision. At the same time, this paper combines the image processing technology to transform the image of the monocular visual athlete into a skeleton model, so as to realize the modeling of the athlete’s movement. In addition, this paper combines the model to explore the indoor and outdoor athlete recovery techniques and validates the model by experiment. The research shows that the research model has certain effects, which can meet the actual needs, and can provide theoretical reference for subsequent related research.
Student sentiment classification model based on GRU neural network and TF-IDF algorithm
Authors: Yu Hailong , Ji Yannan , Li Qinglin
Published: September 2020

Due to the diversity of text expressions, the text sentiment classification algorithm based on semantic understanding is difficult to establish a perfect sentiment dictionary and sentence matching template, which leads to strong limitations of the algorithm. In particular, it has certain difficulties in the classification of student sentiments. Based on this, this paper analyzes the student sentiment classification model by neural network algorithm and uses the student group as an example to explore the application of neural network model in sentiment classification. Moreover, the regularization method is added to the loss function of LSTM so that the output at any time is related to the output at the previous time. In addition, the sentimental drift distribution of sentimental words on each sentimental label is added to the regularizer, and the sentimental information is merged with the two-way LSTM to allow the model to choose forward or reverse. Finally, in order to verify the research model, the performance of the model proposed in this paper is studied through experimental research. The research shows that the model proposed in this paper has better comprehensive performance than the traditional model and can meet the actual needs of students’ sentiment classification.
Improvement of English key competences based on machine learning and artificial intelligence technology
Authors: Zhao Tingting , Cai Yuanyuan
Published: September 2020

How to apply artificial intelligence technology to help education reform is a problem that teaching researchers need to solve urgently. Using artificial intelligence technology to improve the key competences of English subjects is the new direction of current English teaching development. This research combines machine learning technology to analyze the key competences assessment of English teaching disciplines and builds an evaluation model corresponding to the threshold. Moreover, on the basis of orderly mutual information, this study combines the maximum correlation and minimum redundancy theory to select the attribute algorithm to optimize the key competences assessment function of English subjects. In addition, in this study, the performance of the research model is analyzed through a comparative test, and the results are analyzed through actual numerical comparison and error comparison. The research results show that the recognition accuracy of this research model is closer than that of the real score, has higher accuracy, and has certain practical effects.
Recognition of classroom student state features based on deep learning algorithms and machine learning
Authors: Jingchao Hu , Zhang Haiying
Published: September 2020

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.
Simulation of English teaching quality evaluation model based on gaussian process machine learning
Authors: Wenming Huang
Published: September 2020

The efficiency of traditional English teaching quality evaluation is relatively low, and evaluation statistics are very troublesome. Traditional evaluation method makes teaching evaluation a difficult project, and traditional evaluation method takes a long time and has low efficiency, which seriously affects the school’s efficiency. In order to improve the quality of English teaching, based on machine learning technology, this study combines Gaussian process to improve the algorithm, use mixed Gaussian to explore the distribution characteristics of samples, and improve the classic relevance vector machine model. Moreover, this study proposes an active learning algorithm that combines sparse Bayesian learning and mixed Gaussian, strategically selects and labels samples, and constructs a classifier that combines the distribution characteristics of the samples. In addition, this study designed a control experiment to analyze the performance of the model proposed in this study. It can be seen from the comparison that this research model has a good performance in the evaluation of the English teaching quality of traditional models and online models. This shows that the algorithm proposed in this paper has certain advantages, and it can be applied to the practice of English intelligent teaching system.
Intelligent system of English composition scoring model based on improved machine learning algorithm
Authors: Liu Jie , Lin Lin , Liang Xiufang
Published: September 2020

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.
Simulation of stock market investor behavior based on bayesian learning and complex network
Authors: Qianyun Yang , Xiaoyan Wang
Published: September 2020

The increasing complexity of the financial system has increased the uncertainty of the market, which has led to the complexity of the evolution of limited rational investor behavior decisions. Moreover, it also has a negative effect on the market and affects the development of the real economy and social stability. In view of the interconnected characteristics of various elements presented in financial complexity, based on complex network theory, Bayesian learning theory and social learning theory, this study systematically describes the behavioral decision-making mechanism of individual investors and institutional investors from the perspective of network learning. In addition, this study builds an evolutionary model of investor behavior based on Bayesian learning strategies. According to the results of the horizontal and vertical bidirectional studies simulated by experiments, we can see that the method proposed in this study has a certain effect on the evaluation and decision support of stock market investment.
Influence of government subsidy on high-tech enterprise investment based on artificial intelligence and fuzzy neural network
Authors: Ran Li
Published: September 2020

Government subsidies have an important impact on the development of high-interest technology companies and technological innovation. In order to study the relationship between government investment and the development of high-tech enterprises and technological innovation, based on artificial intelligence and fuzzy neural network, this paper builds an analysis model based on artificial intelligence and fuzzy neural network. According to the operation of each loop, this study designs a scheduling strategy that dynamically allocates network utilization according to the dynamic weight of the loop, and periodically changes the sampling period of the system, so that the system can not only run stably but also maximize the use of limited bandwidth. The network resource allocation module allocates the available network bandwidth of each control loop according to the dynamic weight of each loop, and the sampling period calculation module calculates a new sampling period based on the allocated network utilization rate. In addition, in this study, the performance of the model constructed in this paper is analyzed through empirical analysis. The results of the study show that the model constructed in this paper is effective.
Design of industrial internet of things system based on machine learning and artificial intelligence technology
Authors: Kun Xu , Wang Zhiliang , Zhou Ziang , Qi Wang
Published: September 2020

For industrial production, the traditional manual on-site monitoring method is far from meeting production needs, so it is imperative to establish a remote monitoring system for equipment. Based on machine learning algorithms, this paper combines artificial intelligence technology and Internet of Things technology to build an efficient, fast, and accurate industrial equipment monitoring system. Moreover, in view of the characteristics of the diverse types of equipment, scattered layout, and many parameters in the manufacturing equipment as well as the complexity of the high temperature, high pressure, and chemical environment in which the equipment is located, this study designs and implements a remote monitoring and data analysis system for industrial equipment based on the Internet of Things. In addition, based on the application scenarios of the actual aeronautical weather floating platform test platform, this study combines the platform prototype system to design and implement a set of strong real-time communication test platform based on the Windows operating system. The test results show that the industrial Internet of Things system based on machine learning and artificial intelligence technology constructed in this paper has certain practicality.
Behavior recognition based on track space-time characteristics
Authors: Jihong Yang , Lu Yun
Published: September 2020

Under the influence of novel corona virus pneumonia epidemic prevention and control, higher requirements for behavior recognition in complex environment are put forward. The accuracy of traditional methods for sports training is not high, so a method is needed to improve the local action recognition to assist sports training. In the process of behavior recognition, if only the track is regarded as an independent individual, the information of its neighbor will be ignored. Therefore, we use KNN algorithm to get the nearest neighbor trajectory. In order to calculate the rich neighborhood information around the track, this paper calculates the complex relationship between the center track and the neighborhood track from four different angles, including absolute motion, relative motion, distance relationship and direction relationship. Then, from the four different perspectives of variance, discrete coefficient, skewness and kurtosis, this paper proposes a large interval nearest neighbor coding method. This method makes the four eigenvalues complement each other and improves the ability of describing complex and changeable behaviors. The experimental results show that the coding method proposed in this paper can be used for behavior recognition according to different transformation matrix.
Recognition method of outdoor design scene based on support vector machine and feature fusion
Authors: Xiaozhou Yang , Fan Bai , Jones Paul
Published: September 2020

Based on the impact of epidemic prevention and control, the floating population supervision department classifies and controls the floating population by industry. There are many personnel management and control points. When the computer-aided management system is used, the outdoor environment is complex and the data interference is large. Therefore, the recognition accuracy of outdoor scenery is required to be higher. In this paper, a convolutional neural network with adaptive weights is proposed. In this method, the feature fusion strategy is combined with the network, and the optimal feature weight is obtained by training the network. In addition, this paper uses multiple two classifiers instead of multiple classifiers to achieve accurate target classification. Experiments show that the method proposed in this paper has excellent performance in the detection of similar objects. The strategy of replacing multi classification network with multi classification network improves the accuracy and recall of target detection in known environment.
A novel method for solving constrained matrix games with fuzzy payoffs
Authors: Verma Tina
Published: September 2020

In last few years, lots of researchers have proposed different methods to solve the constrained matrix games with fuzzy payoffs. In this paper, it has been shown that the mathematical programming problem of constrained matrix games with fuzzy payoffs, considered by researchers, is mathematically invalid and hence the method, proposed by researchers to obtain the complete solution (minimum expected gain of Player I, maximum expected loss of Player II and their corresponding optimal strategies) of constrained matrix games with fuzzy payoffs by solving the mathematical programming problem with fuzzy payoffs, are also invalid. Further, in the present paper, a new method has been proposed to find the complete solution of matrix games with fuzzy payoffs. To illustrate the proposed method, some existing numerical problems of constrained matrix games with fuzzy payoffs have been solved by the proposed method.
PrefMap: Visualization of fuzzy pairwise preference structures
Authors: Runkler Thomas A.
Published: September 2020

Fuzzy pairwise preferences are an important model to specify and process expert opinions. A fuzzy pairwise preference matrix contains degrees of preference of each option over each other option. Such degrees of preference are often numerically specified by domain experts. In decision processes it is highly desirable to be able to analyze such preference structures, in order to answer questions like: Which objects are most or least preferred? Are there clusters of options with similar preference? Are the preferences consistent or partially contradictory? An important approach for such analysis is visualization. The goal is to produce good visualizations of preference matrices in order to better understand the expert opinions, to easily identify favorite or less favorite options, to discuss and address inconsistencies, or to reach consensus in group decision processes. Standard methods for visualization of preferences are matrix visualization and chord diagrams, which are not suitable for larger data sets, and which are not able to visualize clusters or inconsistencies. To overcome this drawback we propose PrefMap, a new method for visualizing preference matrices. Experiments with nine artificial and real–world preference data sets indicate that PrefMap yields good visualizations that allow to easily identify favorite and less favorite options, clusters, and inconsistencies, even for large data sets.
Reliability analysis for complex systems subject to competing failure processes in an uncertain environment
Authors: Liu Baoliang , Zhang Zhiqiang , Wen Yanqing , Kang Shugui , Guo Yanxin , Qiu Qingan
Published: September 2020

Reliability analysis of complex systems subject to competing failure processes based on probability theory has received increasing attention. However, in many situations, the observed data is too limited to estimate the parameters and probability distributions of the system by statistic methods. To address this problem, an uncertain degradation models is proposed in this paper under the framework of uncertainty theory. Based on this model, a complex system which is subject to both continuous internal degradation and external shocks is introduced. The continuous internal degradation of the system is controlled by some uncertain factors, and the external shocks are deemed to an uncertain renewal reward process. Reliability for the complex systems is obtained by employing the uncertainty theory. Finally, a case study is presented to demonstrate the effectiveness of the results obtained in the paper.
Shipyard location selection based on fuzzy AHP and TOPSIS
Authors: Türk Abdullah , Özkök Murat
Published: September 2020

The shipyard facility location selection (FLS) decision is a critical process that involves conflicting, qualitative, and quantitative criteria. Multi-Attribute Decision Making (MADM) methods are used as a powerful tool to overcome this complex problem. Today, using these methods in an integrated way, more accurate, efficient, and systematic results are obtained in solving complex issues such as FLS, which contains an uncertain structure. This paper proposes a framework for the weighting of criteria and ranking potential feasible locations (alternatives) using the combination of fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) methods. While fuzzy AHP determines the importance values of the criteria by pairwise comparisons, fuzzy TOPSIS prioritizes the alternatives using the relative weights obtained with Fuzzy AHP. The integration of these two techniques provides a robust approach considering the results obtained for the shipyard FLS decision. The applicability of the proposed method is expressed in Turkey by a case study of the shipyard FLS decision.
Single-value neutrosophic cosine measure for evaluation of port logistics competitiveness
Authors: Fan Changxing , Fan En , Chen Jihong , Ye Jun , Zhou Kang
Published: September 2020

Port as an irreplaceable important node in the process of logistics is a special form of the integrated logistics system, which completes the basic logistics service and value-added services in the global supply chain logistics system. At present, the port logistics service has become an important breakthrough in the competition of ports, the improvement of port logistics competitiveness has great influence on the development of port and port city and even the area economic development. Analyzing from the port logistics competitiveness, this paper establishes a comprehensive evaluation index system and proposes a single-value neutrosophic cosine measure method to evaluate the port logistics competitiveness of five sample ports, and gets the score sorting of the logistics competitiveness of these five ports. This method as a helpful tool is clear and easy for port logistics competitiveness evaluation during actual application.
A framework to identify indicators for evaluating car sharing systems
Authors: Min Qu , Yu Chen , Xing-Fu Xiong , Jiang Wu
Published: September 2020

A comprehensive evaluation indicator system is the basis of car sharing systems (CSS) evaluation. The purpose of this study is to introduce the principles and methods of indicator selection for CSS, and to identify indicators for evaluating car sharing systems due to the reason that the importance of indicators can never be overestimated in CSS evaluation. A framework to identify indicators for evaluating CSS is proposed with four steps. First of all, the structure for indicator selection is established with application of AHP method. Secondly, adequacy check and redundancy check are carried out to ensure the structure is adequate and redundant. Thirdly, underlying individual indicators are proposed according to questionnaires. Fourthly, to ensure the necessity, identification, and feasibility of indicators, we conduct N-I-F check. We carry out a case study of CSS evaluation indicators to validate the proposed framework from four dimensions: economic, environmental, systematic, and social. The proposed framework is quantitative and it is helpful in CSS evaluation to identify proper indicators and find out the best CSS option.
Probability multi-valued neutrosophic ELECTRE method for multi-criteria group decision-making
Authors: Zhang Li , Cheng Shufeng , Liu Peide
Published: September 2020

Probability multi-valued neutrosophic sets (PMVNSs) can better describe the incomplete and indeterminate evaluation information, and the ELECTRE method can rank the alternatives in the light of the outranking relations among criteria. To combine their advantages, this paper introduces an extended ELECTRE method to address multi-criteria group decision-making (MCGDM) problems with the information of PMVNSs. Firstly, we introduce the definitions of PMVNSs and the classical ELECTRE method, discuss the ELECTRE-based outranking relations for PMVNSs and analyze some properties of them. Furthermore, the probability multi-valued neutrosophic ELECTRE method is developed to address MCGDM problems based on the proposed distance measure and outranking relations for PMVNSs. Finally, a typical example for logistics outsourcing provider selection is devoted to demonstrate the feasibility of the proposed approach. Moreover, the same example-based comparisons with other existing methods are carried out, the results show our proposed approach outperforms the existing methods in solving the MCGDM problems with PMVNSs.
Multi-robot path planning based on improved artificial potential field and fuzzy inference system1
Authors: Zhao Tao , Li Haodong , Dian Songyi
Published: September 2020

In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems.
Sharp bounds for the general Randić index of transformation graphs
Authors: Imran Muhammad , Akhter Shehnaz , Shaker Hani
Published: September 2020

Inequalities are a useful method to investigate and compare topological indices of graphs relatively. A large collection of graph associated numerical descriptors have been used to examine the whole structure of networks. In these analysis, degree related topological indices have a significant position in theoretical chemistry and nanotechnology. Thus, the computation of degree related indices is one of the successful topic of research. Given a molecular graph H , the general Randić connectivity index is interpreted as R α ( H ) = ∑ ℛ ∈ E ( H ) ( deg H ( a ) deg H ( b ) ) α , with α is a real quantity. Also a graph transformation of H provides a comparatively simpler isomorphic structure with an ease to work on different chemical properties. In this article, we determine the sharp bounds of general Randić index of numerous graph transformations, such that semi-total-point, semi-total-line, total and eight individual transformations H fgh , where f, g, h ∈ {+ , -} of graphs by using combinatorial inequalities.
A neutrosophic clinical decision-making system for cardiovascular diseases risk analysis
Authors: Habib Shaista , us Salam Wardat , Butt M. Arif , Akram M. , Smarandache F.
Published: September 2020

Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirty-five symptoms as input parameters, which are, gender, age, genetic disposition, smoking, blood pressure, cholesterol, diabetes, body mass index, depression, unhealthy diet, metabolic disorder, physical inactivity, pre-eclampsia, rheumatoid arthritis, coffee consumption, pregnancy, rubella, drugs, tobacco, alcohol, heart defect, previous surgery/injury, thyroid, sleep apnea, atrial fibrillation, heart history, infection, homocysteine level, pericardial cysts, marfan syndrome, syphilis, inflammation, clots, cancer, and electrolyte imbalance and finds out the risk of coronary artery disease, cardiomyopathy, congenital heart disease, heart attack, heart arrhythmia, peripheral artery disease, aortic disease, pericardial disease, deep vein thrombosis, heart valve disease, and heart failure. There are five main modules of the system, which are neutrosophication, knowledge base, inference engine, de-neutrosophication, and explainability. To demonstrate the complete working of our system, we design an algorithm and calculates its time complexity. We also present a new de-neutrosophication formula, and give comparison of our the results with existing methods.
Minimal Consequences of CMAH and DBA/2 Backgrounds on a FKRP Deficient Model
Authors: Vaubourg Camille , Gicquel Evelyne , Richard Isabelle , Lostal William , Bellec Jessica
Published: September 2020

Background: Muscular dystrophies (MD) are a large group of genetic diseases characterized by a progressive loss of muscle. The Latent TGFβ Binding Protein 4 (LTBP4) in the DBA/2 background and the Cytidine Monophosphate-sialic Acid Hydroxylase (CMAH) proteins were previously identified as genetic modifiers in severe MD. Objective: We investigated whether these modifiers could also influence a mild phenotype such as the one observed in a mouse model of Limb-Girdle MD2I (LGMD2I). Methods: The FKRPL276I mouse model was backcrossed onto the DBA/2 background, and in separate experiments the Cmah gene was inactivated in FKRPL276I mice by crossing with a Cmah-/- mouse and selecting the double-mutants. The mdx mouse was used as control for these two genome modifications. Consequences at the histological level as well as quantification of expression level by RT-qPCR of genes relevant for muscular dystrophy were then performed. Results: We observed minimal to no effect of the DBA/2 background on the mild FKRPL276I mouse phenotype, while this same background was previously shown to increase inflammation and fibrosis in the mdx mouse. Similarly, the Cmah-/- deletion had no observable effect on the FKRPL276I mouse phenotype whereas it was seen to increase features of regeneration in mdx mice. Conclusions: These modifiers were not observed to impact the severity of the presentation of the mild FKRPL276I model. An interesting association of the CMAH modifier with the regeneration process in the mdx model was seen and sheds new light on the influence of this protein on the dystrophic phenotype.
Correlation Between Quantitative MRI and Muscle Histopathology in Muscle Biopsies from Healthy Controls and Patients with IBM, FSHD and OPMD
Authors: Lassche Saskia , Küsters Benno , Heerschap Arend , Schyns Maxime V.P. , Ottenheijm Coen A.C. , Voermans Nicol C. , van Engelen Baziel G.M.
Published: September 2020

Background: Muscle MRI is increasingly used as a diagnostic and research tool in muscle disorders. However, the correlation between MRI abnormalities and histopathological severity is largely unknown. Objective: To investigate correlations between muscle MRI abnormalities and histopathological severity in healthy controls and patients with muscle disease. Methods: We performed quantitative MRI and histopathological analysis in 35 patients with inclusion body myositis, facioscapulohumeral muscular dystrophy or oculopharyngeal muscular dystrophy and 12 healthy controls. Participants contributed needle biopsies of the vastus lateralis and/or tibialis anterior, yielding 77 muscle biopsies with matched T1, T2 and TIRM MRI imaging. Muscle biopsies were evaluated with a semi-quantitative histopathology severity grading scale (range 0–12) and an inflammation severity grading scale (range 0–3). Results: In muscle disease, histopathology sum scores ranged from 0 to 11 and correlated significantly with fat percentage as measured on MRI (Spearman’s rho = 0.594, p < 0.001). Muscle edema on muscle MRI was associated with increased amounts of inflammation (p < 0.001). Mild abnormalities occured in 95% of control biopsies and were more pronounced in tibialis anterior (median sum score of 1±1 in vastus lateralis and 2±1 in tibialis anterior (p = 0.048)). Conclusion: In muscle disease, fatty infiltration on MRI correlates moderately with muscle histopathology. Histopathological abnormalities can occur prior to the onset of fatty infiltration. In middle-aged controls, almost all biopsies showed some histopathological abnormalities. The findings from this study may facilitate the choice for appropriate imaging sequences as outcome measures in therapeutic trials.
Estimation of Cognitive Performance Based on Premorbid Intelligence in Parkinson’s Disease
Authors: Del Pino Rocio , Díez-Cirarda Maria , Peña Javier , Ibarretxe-Bilbao Naroa , Ojeda Natalia
Published: September 2020

Background: The estimation of premorbid intelligence (PI) is needed for an accurate diagnosis. Objective: This study aimed to estimate the cognitive performance taking into account the PI in Parkinson’s disease (PD) compared to healthy controls (HC); and to analyze the discrepancies between the current and the predicted cognitive performance based on the PI. Method: Semantic fluency, verbal and visual memory, and executive functions were assessed in 39 PD and 162 HC. A linear regression model was used to analyze the discrepancies between the predicted cognitive performance and the current raw scores through PI variables (Word Accentuation Test (WAT), Pseudo-Words (PW) Reading subtest from PROLEC-R, age, and years of education). ROC analyses were performed to assess their diagnostic properties. Results: Significant differences were found in the raw cognitive scores between patients and HC [semantic fluency (t = 6.07; p < 0.001), verbal memory (t = 6.63; p < 0.001), and executive functions (t = 2.57; p = 0.013), and in visual memory (t = 1.97; p = 0.055 marginally significant)]. Compared to HC, PD patients presented higher discrepancies between the predicted cognitive performance and the raw scores in semantic fluency, verbal memory, visual memory, executive functions (AUC = 0.78, 0.78; 0.64, 0.61, respectively). Conclusion: The magnitude of the discrepancies scores between the current and the predicted cognitive performance based on PI indicates the presence of cognitive decline in the specific cognitive domain in PD patients. This study underlines the usefulness of premorbid measures and variables, such as WAT, PW, age, and years of education, to more accurately estimate the cognitive performance in PD.
Coexistent Osteoarthritis and Parkinson’s Disease: Data from the Parkinson’s Foundation Outcomes Project
Authors: Roper Jaimie A. ,   on behalf of the Parkinson’s Foundation Quality Improvement Initiative Investigators, Schmitt Abigail C. , Gao Hanzhi , He Ying , Wu Samuel , Schmidt Peter , Okun Michael S. , Hass Chris J. , Cubillos Fernando
Published: September 2020

Background: The impact of concurrent osteoarthritis on mobility and mortality in individuals with Parkinson’s disease is unknown. Objective: We sought to understand to what extent osteoarthritis severity influenced mobility across time and how osteoarthritis severity could affect mortality in individuals with Parkinson’s disease. Methods: In a retrospective observational longitudinal study, data from the Parkinson’s Foundation Quality Improvement Initiative was analyzed. We included 2,274 persons with Parkinson’s disease. The main outcomes were the effects of osteoarthritis severity on functional mobility and mortality. The Timed Up and Go test measured functional mobility performance. Mortality was measured as the osteoarthritis group effect on survival time in years. Results: More individuals with symptomatic osteoarthritis reported at least monthly falls compared to the other groups (14.5% vs. 7.2% without reported osteoarthritis and 8.4% asymptomatic/minimal osteoarthritis, p = 0.0004). The symptomatic group contained significantly more individuals with low functional mobility (TUG≥12 seconds) at baseline (51.5% vs. 29.0% and 36.1%, p < 0.0001). The odds of having low functional mobility for individuals with symptomatic osteoarthritis was 1.63 times compared to those without reported osteoarthritis (p < 0.0004); and was 1.57 times compared to those with asymptomatic/minimal osteoarthritis (p = 0.0026) after controlling pre-specified covariates. Similar results hold at the time of follow-up while changes in functional mobility were not significant across groups, suggesting that osteoarthritis likely does not accelerate the changes in functional mobility across time. Coexisting symptomatic osteoarthritis and Parkinson’s disease seem to additively increase the risk of mortality (p = 0.007). Conclusion: Our results highlight the impact and potential additive effects of symptomatic osteoarthritis in persons with Parkinson’s disease.