bluuebunny/crossref_metadata_2025 huggingface dataset:
An Exploratory Study of the Development and Pilot Testing of an Interactive Visual Tool of Neuropsychological Test Results in Memory Clinics
Authors: Gruters Angélique A.A. , Ramakers Inez H.G.B. , Stiekema Annemarie P.M. , Verhey Frans R.J. , Kessels Roy P.C. , de Vugt Marjolein E.
Published: December 2020

Background:

Neuropsychological feedback is an important part of the neuropsychological assessment process. However, patients have difficulties remembering this information.

Objective:

The aim of this study was to develop a web-based visual tool to improve the understanding of neuropsychological results, information retention, and psychologist-patient communication.

Methods:

The visual tool was developed and optimized using an iterative three-phase stepwise approach to determine its usability, technology acceptance, and feasibility in a memory clinic population. Feedback from different user perspectives (patients, family members, and psychologists) was obtained in each phase using a multimethod approach (e.g. a multidisciplinary brainstorm session, think-aloud sessions, focus groups). The prototype was subsequently tested in a pilot study.

Results:

The first phases offered insights that led to optimization of the prototype. On a scale ranging from 0 to 100, psychologists evaluated the usability as high [88.1±7.6,70–87]. During the pilot study, both patients and significant others gave positive feedback, but information retention in patients remained low. All participants thought the benefits of the visual tool included seeing cognitive strengths and weaknesses with a translation to daily life all at one glance and receiving feedback on paper to take home. Important barriers were mentioned by psychologists, such as a limited set of tests included and no integration with hospital systems.

Conclusion:

Overall, patients, family members, and psychologists reported that a visual display of the cognitive profile with insights into daily life had added value to clinical practice. Feedback from the pilot study was adopted in the tool for future implementation purposes.
Word order detection in English classroom teaching based on improved genetic algorithm of block coding
Authors: Bowen Deng
Published: December 2020

The performance of the speech recognition system for English classroom teaching is largely affected by the surrounding environment. These interference signals will seriously reduce the quality and intelligibility of the speech signal, thereby greatly reducing the performance of the far-field speech recognition system. Aiming at word order detection in English classroom teaching, this paper proposes an analysis model based on block coding and improved genetic algorithm. Moreover, for DNN-based single-channel speech enhancement algorithms, this paper proposes PDNNs and PLSTMs to solve the problem of serious performance degradation of prototype DNN speech enhancement under low signal-to-noise ratio. This method decomposes the entire enhancement task into multiple subtasks to complete, and the previously completed subtasks will provide prior knowledge for the subsequent subtasks, so that the subsequent subtasks can learn its goals better. In general, the experimental results prove the reliability of the model constructed in this paper.
Comprehensive efficiency analysis of logistics industry based on machine learning and self-service data envelopment analysis model
Authors: Man Huang , Jie Lian
Published: December 2020

The research on operational efficiency focuses on the macro-level research. However, there are relatively few studies on the industry level. In particular, there are fewer studies on the logistics industry, which has a leading and fundamental significance in the national economic system and is regarded as the third important source of profit. Moreover, scholars are more focused on the research on the operational performance and profitability of logistics enterprises. In order to study the efficiency of the logistics industry, this paper uses machine learning technology as the foundation and self-service data envelopment analysis to construct a comprehensive efficiency analysis model for the logistics industry. Moreover, this paper adopts a combination of qualitative and quantitative analysis to conduct empirical research on the operational efficiency and influencing factors of the logistics industry to explore the factors that affect the operational efficiency of logistics enterprises. In addition, this article optimizes the model data through statistics, and compares the model analysis data with the actual situation. It can be seen from the research results that the model constructed in this paper has a certain effect.
Regression analysis of supply chain financial risk based on machine learning and fuzzy decision model
Authors: Xuan Fan
Published: December 2020

Supply chain financial risk control has become the biggest obstacle restricting financial institutions from developing supply chain financial services. If financial institutions want to make a difference in the supply chain finance business, they need to implement strong control measures against supply chain finance risks. Based on the research of supply chain finance and risk related theories, this paper uses fuzzy preference relations and selects the main risk criteria to construct a risk evaluation index system. Moreover, this paper takes the development of J company’s supply chain finance business as a background to conduct an empirical analysis and proposes the risk management measures and development strategies of J company’s actual supply chain finance business. The combination of quantitative analysis and qualitative evaluation is more comprehensive and operational value for the research and management of supply chain financial risks. In addition, this paper uses the evaluation index system constructed in this paper to conduct a more comprehensive summary and analysis of the internal and external environment of risk influencing factors. The research results show that the performance of the model constructed in this paper is good.
Performance evaluation of English learning through computer mode using neural network and AI techniques
Authors: Nianfan Peng
Published: December 2020

Learning performance evaluation is an important part of computer English teaching. Through evaluation, students can understand whether they have reached the learning goals of a lesson, and what are their shortcomings, so that they can continuously improve and improve themselves against these shortcomings. Combining actual needs, based on neural network and artificial intelligence technology, this paper constructs a computer English learning performance evaluation model based on machine learning. The computer English learning performance evaluation system in this paper is constructed on the basis of auditory characteristics and introduces a wavelet entropy feature based on the best tree wavelet packet decomposition, and it is applied to the establishment of an adaptive model. Moreover, this paper uses controlled experiments to analyze the model performance and combines mathematical statistics to visually display the model effect. From the research results, it can be seen that the performance of the model constructed in this paper basically meets the expected requirements and actual needs.
Image modal analysis in art design and image recognition using AI techniques
Authors: Yangxiaoxiao Zhou
Published: December 2020

Traditional art design models are inefficient and difficult to control the modal characteristics of images. Based on the actual needs of artistic design, this paper builds an intelligent model of artistic design image modal analysis based on machine learning technology and image recognition technology, and proposes a feature extraction method based on wavelet transform and fuzzy logic and an art image classification method based on rotating quaternion wavelet transform. Moreover, this paper calculates the activation intensity value corresponding to each fuzzy area in the fuzzy feature space and normalizes these activation intensity values to form the artistic image feature vector. In addition, this paper uses rotating quaternary wavelet transform to decompose the art image, and then calculate the energy and standard deviation of each decomposed sub-band coefficient. Finally, this paper uses support vector machines to recognize artistic images. The experimental research shows that the model constructed in this paper has certain effects.
Simulation of athlete gait recognition based on spectral features and machine learning
Authors: Wang Linuo
Published: December 2020

The current technology related to athlete gait recognition has shortcomings such as complicated equipment and high cost, and there are also certain problems in recognition accuracy and recognition efficiency. In order to improve the efficiency of athletes’ gait recognition, this paper studies the different recognition technologies of athletes based on machine learning and spectral feature technology and applies computer vision technology to sports. Moreover, according to the calf angular velocity signal, the occurrence of leg movement is detected in real time, and the gait cycle is accurately divided to reduce the influence of the signal unrelated to the behavior on the recognition process. In addition, this study proposes a gait behavior recognition method based on event-driven strategies. This method uses a gyroscope as the main sensor and uses a wearable sensor node to collect the angular velocity signals of the legs and waist. In addition, this study analyzes the performance of the algorithm proposed by this paper through experimental research. The comparison results show that the method proposed by this paper has improved the number of recognition action types and accuracy and has certain advantages from the perspective of computation and scalability.
Similarity detection of English text and teaching evaluation based on improved TCUSS clustering algorithm
Authors: Wang Yu
Published: December 2020

The semantic similarity calculation task of English text has important influence on other fields of natural language processing and has high research value and application prospect. At present, research on the similarity calculation of short texts has achieved good results, but the research result on long text sets is still poor. This paper proposes a similarity calculation method that combines planar features with structured features and uses support vector regression models. Moreover, this paper uses PST and PDT to represent the syntax, semantics and other information of the text. In addition, through the two structural features suitable for text similarity calculation, this paper proposes a similarity calculation method combining structural features with Tree-LSTM model. Experiments show that this method provides a new idea for interest network extraction.
Research on basketball sports neural network model based on nonlinear classification
Authors: Yang Yongfen
Published: December 2020

Intelligent video analysis has broad application prospects. How to automatically analyze and identify human behavior in video has attracted extensive attention from researchers at home and abroad. Moreover, researching effective video behavior recognition algorithms and designing efficient behavior recognition systems has important theoretical and practical value. This paper studies the nonlinear classification technique and applies the video behavior recognition algorithm to basketball recognition. Moreover, this paper studies the classical convolutional neural network model and several improvements. In addition, this paper explains the advantages of convolutional neural networks in feature extraction compared with traditional neural networks and analyzes the performance of the algorithm by designing actual experiments. The research results show that the algorithm can quickly identify multiple players on the field, and the method can effectively deal with occlusion and other issues with high accuracy and real-time.
Dairy brand loyalty measurement model based on machine learning clustering algorithm
Authors: Liu Xiujuan , Zhao Huifeng
Published: December 2020

At present, the dairy brand loyalty evaluation model is not perfect, and the dairy brand loyalty measurement model for the consumer-oriented industry needs to be further studied. Through machine learning methods, online consumer brand product purchase behaviors are clustered to achieve clustering of users with similar loyalty and to measure online dairy brand loyalty. This study has the advantages of applying machine learning to processing online consumer big data, that is, it has advantages when processing high-dimensional data, when processing data in multiple ways, and when analyzing data with high complexity algorithms. The independent variables, dependent variables, and adjusted variables in the model are measured in the form of a Likert five-level scale. Moreover, this study combines with actual cases to make adjustments to the measurement of dairy brand loyalty and verifies the model performance through simulation experiments. The research results show that the validity of the scale structure is good, and the research model has certain practical effects.
New PQ theory for power quality improvement using modular multilevel converter based UPFC system
Authors: Thamizh Thentral T.M. , Jegatheesan R. , Subramani C.
Published: December 2020

The source current harmonics reduction techniques were found to be unpredictable and disparity under different loading conditions. The presence of uncertainity issue in harmonics elimination is due to nonlinear loads. Filters can be used to eliminate the harmonics and power quality issues. But these filters are not cost effective to provide dynamic performance under various loading conditions. The target of this paper is to minimize source current harmonics with optimum voltage stability under different loading conditions. A new Unified Power Flow controller is developed whose series compensator is replaced by modular multilevel converter to achieve high modular level with reduced harmonics and fast current limiting during the fault short circuit and shunt compensator is replaced with four switches and one capacitor combination to achieve the twin benefit of more reliable power system and good voltage stability for different loadings. DDSRF (Decoupled Double Synchronous Reference Frame) theory is utilized in the proposed converter for generating the reference current from the AC supply. DDSRF theory generates sinusoidal harmonics with the opposite phase to the load current. The UPFC can suck or injects the responsive power in the PCC. After DDSRF theory, hysteresis controller is used to produce PWM pulse for the shunt and series compensator. The proposed DDSRF theory is compared with existing dq theory to show its effectiveness in terms of THD analysis. The PI and fuzzy logic methodology is utilized to control the capacitor DC rail voltage. The proposed approach is simulated using Matlab under various loading condition and hardware is developed using Spartan 6E FPGA Controller.
Using learning time as metrics: an artificial intelligence driven risk assess framework to evaluate DDoS cyber attack
Authors: Chen Yen-Hung , Chang Arthur , Huang ChunWei
Published: December 2020

The cloud computing and Internet of Things (IoT) have become two key technologies to meet future business requirements. However, a massive scale of Distributed Denial-of-Service (DDoS) has been widely applied to congest network critical links and to paralyze the cloud and IoT service. This is mainly due to DDoS is easily implemented, obfuscated, and occulted by launching large-scale legitimate low-speed flows and rolling target links to paralyze target network areas. Many metrics and risk access management frameworks to evaluate the impact of DDoS are proposed. However, they all lack time granularity to evaluate the cost of different scales of attacks in IoT or large-scale network structure. This study proposes an AI Driven Evaluation framework, called ADE, that applies Convolution Neural Networks to statistically evaluate the network status through end-to-end functionality (Input: network status; Output: DDoS detected or not) without any manual intervention. ADE provides quantitative security risk analysis by using learning time as the control variable, network structure as the independent variable, and time to identify DDoS as the dependent variable. The learning time to detect DDoS event and recover the system is then applied to evaluate the scale of this DDoS, the reasonability of the regulated RTO, and the vulnerability of the current net-work topology and the improvement due to the new security solution. The experiment results demonstrate the contributions of ADE are (1) providing objective and quantitative analytical security risk assessment indicator, (2) providing an autonomic DDoS defense framework without any manual intervention which allows cloud computing and Internet of Things company focuses on their service and leaves security defending to ADE, and (3) demonstrating the possibility of AI assisted risk assessment which enables security defense solution buyer with less security domain experts to evaluate suitable network defense strategy.
Extended CPT-TODIM method for interval-valued intuitionistic fuzzy MAGDM and its application to urban ecological risk assessment
Authors: Zhao Mengwei , Wei Guiwu , Wei Cun , Wu Jiang , Wei Yu
Published: December 2020

The urban ecological risk assessment is a new research field, which has been rising and developing with the change of environment management objectives and environment conception. The urban ecological risk assessment could be regarded as a classical multi-attribute group decision making (MAGDM) issue. The interval-valued intuitionistic fuzzy set (IVIFS) can fully describe the uncertain information for the urban ecological risk assessment. Furthermore, the classical TODIM (an acronym in Portuguese for Interactive Multi-Criteria Decision Making) is built on cumulative prospect theory (CPT), which is a selectable method in reflecting the DMs’ psychological behavior. Thus, in this paper, the TODIM method based on the CPT is proposed for MAGDM issue under IVIFS. At the same time, it is enhancing rationality to get the weight information of attributes by using the interval-valued intuitionistic fuzzy entropy weight method. And focusing on hot issues in contemporary society, this article applies the discussed method to urban ecological risk assessment, and demonstrates urban ecological risk assessment model based on the proposed method. Finally, through comparing the outcome of comparative analysis, we conclude that this improved approach is acceptable.
A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction
Authors: Yang Dongqi , Zhang Wenyu , Wu Xin , Ablanedo-Rosas Jose H. , Yang Lingxiao , Yu Wangzhi
Published: December 2020

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.
Spatiotemporal spectral histogramming analysis in hand gesture signature recognition
Authors: Khoh Wee How , Pang Ying Han , Ooi Shih Yin , Yap Hui Yen
Published: December 2020

Dynamic signature recognition emerges to perfectly solve the hygiene concern due to its no-contact characteristic. Nevertheless, the recognition of dynamic texture is challenging compared with the static signature image due to their unknown spatial and temporal nature. In this work, we present a multi-view spatiotemporal approach based on spectral histogramming for hand gesture signature recognition. A Microsoft Kinect sensor is adopted to capture the motion of signing in a sequence of depth frames. The depth frame sequence is viewed from three directional sights to retrieve rich information, such as temporal changes at each spatial location, the signing motion flow of each vertical and horizontal spatial space in a temporal manner. Furthermore, the proposed approach performs feature description on different levels of locality. This function enables a multi-resolution analysis on this dynamic signature. The robustness of the proposed approach is reflected with the promising result by striking the state-of-the-art performance, as substantiated in the empirical results.
A SWOT method to evaluate safety risks in life cycle of wind turbine extended by D number theory
Authors: Mo Hongming
Published: December 2020

Wind power is a typical clean and renewable energy, which has been widely regarded as one of the replaceable energies in many countries. Wind turbine is the key equipment to generate wind power. It is necessary to evaluate the risks of each stage of the wind turbine with regard to occupational health and safety. In this study, the stage of production of life cycle of wind turbine is considered. The aim of this study is to propose a new method to identify and evaluate the risk factors based on strengths-weaknesses-opportunities-threats (SWOT) analysis and D number theory, named D-SWOT method. A wind turbine firm is used to demonstrate the detailed steps of the proposed method. SWOT is conducted to identify the risk factors of production stage of the wind turbine company. Experts are invited to perform the risk assessment, and D number theory is carried out to do the processes of information representation and integration. After that, some suggestions are provided to the company to lower the risks. The D-SWOT method obtains the same results as the previous method of hesitant fuzzy linguistic term set (HFLTS). Compared with HFLTS method, D-SWOT method simplifies the process of information processing, and D-SWOT method is more intuitional and concise. Besides, a property of pignistic probability transformation of D number theory (DPPT) is proposed in the manuscript, which extends D number theory and has been used in the process of decision making of D-SWOT.
Site selection of high-speed railway station: A trapezoidal fuzzy neutrosophic-based consensual group decision-making approach
Authors: Wang Rui , Chen Zhen-Song , Shuai Bin , Chin Kwai-Sang , Martínez Luis
Published: December 2020

The need of quick and comfortable public transportation in our societies makes that many countries are planning to develop a high-speed railway network for improving their passenger transport capacity. Such a development implies among other problems how to select the location of high-speed railway station (HSRS) in each city along the line? Thus, this paper introduces an integrated approach for solving the site selection problem of HSRS, which consists of a consensus reaching process (CRP) with a group decision making (GDM) method whose inputs are trapezoidal fuzzy neutrosophic set to model experts’ assessments of potential locations of HSRS. To accomplish the decision process, the inputs should be weighted and aggregated with novel trapezoidal fuzzy neutrosophic prioritized aggregation operators to reflect the priority relationship between the aggregated information. The necessity of polishing conflicts in these decisions lead then to improve the experts’ agreement in the group, in our case, three consensus indexes at different levels are defined to implement the CRP. Eventually, the proposed CRP-GDM approach is put forward to solve the site selection issue of HSRS and a case of study is presented to illustrate its applications and advantages.
Elucidation of the Genetic Cause in Dutch Limb Girdle Muscular Dystrophy Families: A 27-Year’s Journey
Authors: ten Dam L. , de Visser M. , Ginjaar Ieke B. , van Duyvenvoorde Hermine A. , van Koningsbruggen Silvana , van der Kooi Anneke J.
Published: December 2020

Background: A Dutch cohort of 105 carefully selected limb girdle muscular dystrophy (LGMD) patients from 68 families has been subject to genetic testing over the last 20 years. After subsequent targeted gene analysis around two thirds (45/68) of the families had received a genetic diagnosis in 2013. Objective: To describe the results of further genetic testing in the remaining undiagnosed limb girdle muscular dystrophy families in this cohort. Methods: In the families of the cohort for whom no genetic diagnosis was established (n = 23) further testing using Sanger sequencing, next generation sequencing with gene panel analysis or whole-exome sequencing was performed. In one case DNA analysis for facioscapulohumeral dystrophy type 1 was carried out. Results: In eight families no additional genetic tests could be performed. In 12 of the remaining 15 families in which additional testing could be performed a genetic diagnosis was established: two LGMDR1 calpain3-related families with CAPN3 mutations, one LGMDR2 dysferlin-related family with DYSF mutations, three sarcoglycanopathy families (LGMDR3-5 α-, β- and γ-sarcoglycan-related) with SGCA/SGCB/SGCG mutations, one LGMDR8 TRIM 32-related family with TRIM32 mutations, two LGMDR19 GMPPB-related families with GMPPB mutations, one family with MICU1-related myopathy, one family with FLNC-related myopathy and one family with facioscapulohumeral dystrophy type 1. At this moment a genetic diagnosis has been made in 57 of the 60 families of which DNA was available (95%). Conclusion: A genetic diagnosis is obtained in 95% of the families of the original Dutch LGMD cohort of which DNA was available.
Psychometric Properties of the Clinical Dementia Rating Scale Sum of Boxes in Parkinson’s Disease
Authors: Gallagher Julia , Rick Jacqueline , Xie Sharon X. , Martinez-Martin Pablo , Mamikonyan Eugenia , Chen-Plotkin Alice , Dahodwala Nabila , Morley James , Duda John E. , Trojanowski John Q. , Siderowf Andrew , Weintraub Daniel
Published: December 2020

Background: A composite measure that assesses both cognitive and functional abilities in Parkinson’s disease (PD) would be useful for diagnosing mild cognitive impairment (MCI) and PD dementia (PDD) and as an outcome measure in randomized controlled trials. The Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB) was designed to assess both cognition and basic-instrumental activities of daily living in Alzheimer’s disease but has not yet been validated in PD. Objective: To validate the CDR-SOB as a composite cognitive-functional measure for PD patients, as well as to assess its sensitivity to change. Methods: The CDR-SOB and a comprehensive cognitive and functional battery was administered to 101 PD patients at baseline (39 normal cognition [NC], 41 MCI and 21 PDD by expert consensus panel), and re-administered to 64 patients after 1-2 years follow-up (32 NC and 32 cognitive impairment [CI] at baseline). Results: Cross-sectionally, CDR-SOB and domain scores were correlated with corresponding neuropsychological or functional measures and were significantly different between cognitive subgroups both at baseline and at follow-up. In addition, CDR-SOB ROC curves distinguished between normal cognition and dementia with high sensitivity, but did not distinguish well between NC and MCI. Longitudinal changes in the CDR-SOB and domain scores were not significant and were inconsistent in predicting change in commonly-used cognitive and functional tests. Conclusion: The CDR-SOB detects dementia-level cognitive impairment in PD but may not be appropriate for predicting longitudinal combined cognitive-functional changes in patients without significant cognitive impairment at baseline.
Expression of Transcription Factors in CD4 + T Cells as Potential Biomarkers of Motor Complications in Parkinson’s Disease
Authors: Contaldi Elena , Magistrelli Luca , Milner Anna Vera , Cosentino Marco , Marino Franca , Comi Cristoforo
Published: December 2020

Background: Management of motor complications (MC) represents a major challenge in the long-term treatment of Parkinson’s disease (PD) patients. In this context, the role of peripheral adaptive immunity may provide new insights, since neuroinflammatory mechanisms have been proved crucial in the disease. Objective: The aim of this study was to analyze the transcription factors genes involved in CD4 + T cells development to uncover specific molecular signatures in patients with (PMC) and without (WMC) motor complications. Methods: mRNA levels of CD4 + T lymphocytes transcription factor genes TBX21, STAT1, STAT3, STAT4, STAT6, RORC, GATA3, FOXP3, and NR4A2 were measured from 40 PD patients, divided into two groups according to motor complications. Also, 40 age- and sex-matched healthy controls were enrolled. Results: WMC patients had higher levels of STAT1 and NR4A2 (p = 0.004; p = 0.003), whereas in PMC we found higher levels of STAT6 (p = 0.04). Also, a ROC curve analysis confirmed STAT1 and NR4A2 as feasible biomarkers to discriminate WMC (AUC = 0.76, 95%CI 0.59–0.92, p = 0.005; AUC = 0.75, 95%CI 0.58–0.90, p = 0.007). Similarly, STAT6 detected PMC patients (AUC = 0.69, 95%CI 0.52–0.86, p = 0.037). Conclusion: These results provide evidence of different molecular signatures in CD 4 + T cells of PD patients with and without MC, thus suggesting their potential as biomarkers of MC development.
Determination of the working conditions of oncology nurses as professional risks: A qualitative study
Authors: Akimova Natalia Alexandrovna , Medvedeva Elena Nikolaevna , Andriyanova Elena Andreevna , Chernyshkova Elena Vyacheslavovna
Published: December 2020

BACKGROUND: Nurses often face professional risks in their work. Most research in Russia dedicated to professional risks of nurses are quantitative studies and deal with a limited range of issues. There is an apparent lack of qualitative studies on this problem. OBJECTIVE: The aim of this research is to study opinions of oncology nurses concerning their working conditions from the point of view of their professional risks assessment and their minimization. METHOD: The research was conducted by an independent researcher using a method of semi-structured interview. The content-analysis with inductive approach was used for the data analysis. RESULTS: According to the data analysis there was singled out five main topics, in particular, professional risks that are appreciated by nurses, assessment of professional risks as employment obstacles, permitted ways of psychoemotional risks mastering, permitted ways of physical exertion mastering, effective external mechanisms of risks mastering. In general, nurses do not consider their professional risks as employment obstacles. They lay special emphasis on psychosocial risks, which are the core of all other professional risks. CONCLUSION: This research shows the opinion of the nursing service heads of oncology departments about professional risks arising in the process of their staff performing the duties. The results of the research demonstrate that the main perceived dangers are psychosocial, biological and chemical risks specific to the work with oncology patients. Possible ways to reduce the impact of professional risks on nurses can be both external institutional mechanisms and internal personal resources. Determining the working conditions of oncological nurses allows to manage occupational risks, to form a valuable attitude to health, as well as to identify indicators that form a social perception of the profession. The study of the working conditions of oncological nurses allows to manage professional risks, form a value-based attitude to health, and also identify indicators that form a social idea of the profession.
Preclinical models of frailty: Focus on interventions and their translational impact: A review
Authors: Mishra Manish , Howlett Susan E.
Published: December 2020

The concept of frailty refers to heterogeneity in the risk of adverse outcomes for people of the same age. It is traditionally thought of as the inability of the body to maintain homeostasis. It can help explain differences between chronological and biological age and can quantify healthspan in experimental studies. Although clinical studies have developed tools to quantify frailty over the past two decades, preclinical models of frailty have only recently been introduced. This review describes the notion of frailty and outlines two commonly used clinical approaches to quantify frailty: the frailty phenotype and the frailty index. Translation of these methodologies for use in animals is introduced and studies that use these models to evaluate interventions designed to attenuate or exacerbate frailty are discussed. These include studies involving manipulation of diet, implementation of exercise regimens and tests of pharmaceutical agents to exacerbate or attenuate frailty. Together, this body of work suggests that preclinical frailty assessment tools are a valuable new resource to quantify the impact of interventions on overall health. Future studies could deploy these models to evaluate new frailty therapies, test combinations of interventions and assess interventions to enhance the ability to resist stressors in the setting of ageing.
Enhancing sensory acuity and balance function using near-sensory biofeedback-based perturbation intervention for individuals with traumatic brain injury
Authors: Veerubhotla Akhila , Pilkar Rakesh , Ehrenberg Naphtaly , Nolan Karen J.
Published: December 2020

BACGROUND: Interventions addressing balance dysfunction after traumatic brain injury (TBI) only target compensatory aspects and do not investigate perceptual mechanisms such as sensory acuity. OBJECTIVE: To evaluate the efficacy of a novel intervention that integrates sensory acuity with a perturbation-based approach for improving the perception and functional balance after TBI. METHODS: A two-group design was implemented to evaluate the effect of a novel, perturbation-based balance intervention. The intervention group (n = 5) performed the intervention with the sinusoidal (0.33, 0.5, and 1 Hz) perturbations to the base of support with amplitudes derived using our novel outcome of sensory acuity - perturbation perception threshold (PPT). The efficacy is evaluated using changes in PPT and functional outcomes (Berg Balance Scale (BBS), Timed-up and Go (TUG), 5-meter walk test (5MWT), and 10-meter walk test (10MWT)). RESULTS: There was a significant post-intervention change in PPT for 0.33 Hz (p = 0.021). Additionally, clinically and statistically significant improvements in TUG (p = 0.03), 5MWT (p = 0.05), and 10MWT (p = 0.04) were observed. CONCLUSIONS: This study provides preliminary efficacy of a novel, near-sensory balance intervention for individuals with TBI. The use of PPT is suggested for a comprehensive understanding and treatment of balance dysfunction. The promising results support the investigation in a larger cohort.
Representing narratives in digital libraries: The narrative ontology
Authors: Meghini Carlo , Bartalesi Valentina , Metilli Daniele
Published: December 2020

Digital Libraries (DLs), especially in the Cultural Heritage domain, are rich in narratives. Every digital object in a DL tells some kind of story, regardless of the medium, the genre, or the type of the object. However, DLs do not offer services about narratives, for example it is not possible to discover a narrative, to create one, or to compare two narratives. Certainly, DLs offer discovery functionalities over their contents, but these services merely address the objects that carry the narratives (e.g. books, images, audiovisual objects), without regard for the narratives themselves. The present work aims at introducing narratives as first-class citizens in DLs, by providing a formal expression of what a narrative is. In particular, this paper presents a conceptualisation of the domain of narratives, and its specification through the Narrative Ontology (NOnt for short), expressed in first-order logic. NOnt has been implemented as an extension of three standard vocabularies, i.e. the CIDOC CRM, FRBRoo, and OWL Time, and using the SWRL rule language to express the axioms. On the basis of NOnt, we have developed the Narrative Building and Visualising (NBVT) tool, and applied it in four case studies to validate the ontology. NOnt is also being validated in the context of the Mingei European project, in which it is applied to the representation of knowledge about Craft Heritage.
Sequentially applied myoelectrically controlled FES in a task-oriented approach and robotic therapy for the recovery of upper limb in post-stroke patients: A randomized controlled pilot study
Authors: Perini Gloria , Bertoni Rita , Thorsen Rune , Carpinella Ilaria , Lencioni Tiziana , Ferrarin Maurizio , Jonsdottir Johanna
Published: December 2020

BACKGROUND: Functional recovery of the plegic upper limb in post-stroke patients may be enhanced by sequentially applying a myoelectrically controlled FES (MeCFES), which allows the patient to voluntarily control the muscle contraction during a functional movement, and robotic therapy which allows many repetitions of movements. OBJECTIVE: Evaluate the efficacy of MeCFES followed by robotic therapy compared to standard care arm rehabilitation for post-stroke patients. METHODS: Eighteen stroke subjects (onset ⩾ 3 months, age 60.1 ± 15.5) were recruited and randomized to receive an experimental combination of MeCFES during task-oriented reaching followed by robot therapy (MRG) or same intensity conventional rehabilitation care (CG) aimed at the recovery of the upper limb (20 sessions/45 minutes). Change was evaluated through Fugl-Meyer upper extremity (FMA-UE), Reaching Performance Scale and Box and Block Test. RESULTS: The experimental treatment resulted in higher improvement on the FMA-UE compared with CG (P= 0.04), with a 10-point increase following intervention. Effect sizes were moderate in favor of the MRG group on FMA-UE, FMA-UE proximal and RPS (0.37–0.56). CONCLUSIONS: Preliminary findings indicate that a combination of MeCFES and robotic treatment may be more effective than standard care for recovery of the plegic arm in persons > 3 months after stroke. The mix of motor learning techniques may be important for successful rehabilitation of arm function.
Research on blood oxygen activity in cerebral cortical motor function areas with adjustment intention during gait
Authors: Li Chunguang , Zhu Yufei , Qu Wei , Sun Lining
Published: December 2020

BACKGROUND: The study of the neural mechanism of human gait control can provide a theoretical basis for the treatment of walking disorders or the improvement of rehabilitation strategies, and further promote the functional rehabilitation of patients with movement disorders. However, the performance and changes of cerebral cortex activity corresponding to gait adjustment intentions are still not clear. OBJECTIVE: The purpose of this study was to detect the blood oxygen activation characterization of the cerebral cortex motor function area when people have the intention to adjust gait during walking. METHODS: Thirty young volunteers (21 ± 1 years old) performed normal walking, speed increase, speed reduction, step increase, and step reduction, during which oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total oxyhemoglobin (HbT) information in the prefrontal cortex (PFC), premotor cortex (PMC), supplementary motor area (SMA) was continuous monitored using near-infrared brain functional imaging. RESULTS: (1) With the intention to adjust gait, the HbO concentration in the SMA increased significantly, while the HbT concentration in the medial-PFC decreased significantly. (2) In the HbO concentration, step reduction is more activated than the step increase in the left-PMC (p= 0.0130); step adjustment is more activated than speed adjustment in the right-PMC (p= 0.0067). In the HbR concentration, the speed reduction is more activated than the speed increase in the left-PFC (p= 0.0103). CONCLUSIONS: When the intention of gait adjustment occurs, the increase of HbO concentration in the SMA indicates the initial stage of gait adjustment will increase the cognitive-locomotor demand of the brain. The left brain area meets the additional nerve needs of speed adjustment. The preliminary findings of this study can lay an important theoretical foundation for the realization of gait control based on fNIRS-BCI technology.
Vestibular migraine and recurrent vertigo of childhood: Diagnostic criteria consensus document of the Classification Committee of Vestibular Disorders of the Bárány Society and the International Headache Society
Authors: van de Berg Raymond , Widdershoven Josine , Bisdorff Alexandre , Evers Stefan , Wiener-Vacher Sylvette , Cushing Sharon L. , Mack Kenneth J. , Kim Ji Soo , Jahn Klaus , Strupp Michael , Lempert Thomas
Published: December 2020

This paper describes the diagnostic criteria for “Vestibular Migraine of Childhood”, “probable Vestibular Migraine of Childhood” and “Recurrent Vertigo of Childhood” as put forth by the Committee for the Classification of Vestibular Disorders of the Bárány Society (ICVD) and the Migraine Classification subgroup of the International Headache Society. Migraine plays an important role in some subgroups of children with recurrent vertigo. In this classification paper a spectrum of three disorders is described in which the migraine component varies from definite to possibly absent. These three disorders are: Vestibular Migraine of Childhood, probable Vestibular Migraine of Childhood and Recurrent Vertigo of Childhood. The criteria for Vestibular Migraine of Childhood (VMC) include (A) at least five episodes with vestibular symptoms of moderate or severe intensity, lasting between five minutes and 72 hours, (B) a current or past history of migraine with or without aura, and (C) at least half of episodes are associated with at least one migraine feature. Probable Vestibular Migraine of Childhood (probable VMC) is considered when at least three episodes with vestibular symptoms of moderate or severe intensity, lasting between five minutes and 72 hours, are accompanied by at least criterion B or C from the VMC criteria. Recurrent Vertigo of Childhood (RVC) is diagnosed in case of at least three episodes with vestibular symptoms of moderate or severe intensity, lasting between 1 minute and 72 hours, and none of the criteria B and C for VMC are applicable. For all disorders, the age of the individual needs to be below 18 years old. It is recommended that future research should particularly focus on RVC, in order to investigate and identify possible subtypes and its links or its absence thereof with migraine.
The relationship between safety attitude and safety performance in the petrochemical industry: The mediating role of safety climate
Authors: Karimpour Sanaz , Jahani Fereshteh , Shirali Gholam Abbas
Published: December 2020

BACKGROUND: This study aimed to investigate the relationship between safety attitudes and safety performance considering the mediating role of safety climate in the petrochemical industry. METHOD: The statistical population of this study included all 1700 employees in the petrochemical industry of Iran, among whom 320 were randomly selected as the participants and completed the research questionnaire. Then, the validity (content, convergent, and divergent) and reliability (Cronbach’s alpha and composite reliability) of the instrument were examined, and the research hypotheses were tested using Smart partial least squares (PLS) software. RESULTS: The results showed that the model has goodness of fit and, thereby, the positive effect of safety attitude on safety performance was confirmed. In addition, the mediating role of safety climate in the relationship between safety attitude and safety performance was proved. That is, 39.6% of the total effect of safety attitude on safety performance was explained through the mediating role of safety climate. CONCLUSION: The findings of this study can improve safety culture and bring about excellent safety performance in petrochemical industries.
QIGOON SYSTEM AND YOGA THERAPY AS A MEANS OF PHYSICAL REHABILITATION OF PATIENTS WITH HYPERTENSION
Authors: Kopochynska Yuliya , Kovalenko Natalia
Published: December 2020

The article considers the features of the etiopathogenesis of hypertension and the use of methods and means of physical therapy, which combine diet therapy, exercise therapy, swimming, therapeutic massage, physiotherapy, cardio, etc. However, existing physical therapy programs do not have a holistic approach to restoring the health of people with hypertension, so the article explores alternative approaches to the use of health programs for the rehabilitation of patients with hypertension, in particular, the Qigong system and yogatherapy. The developed program of physical rehabilitation for hypertension allows to reduce the index of the area of ​​hypertension, which increases the effectiveness of rehabilitation treatment, has a positive effect on the psycho-emotional and physical condition of the patient, helps to normalize blood pressure, accelerates recovery and recovery.
FATORES DE RISCOS ASSOCIADOS À LEPTOSPIROSE EM EQUÍDEOS: REVISÃO DE LITERATURA
Authors: Alves Pinto Paulo Acioli , Teles José Andreey Almeida , Furtado Gil Dutra
Published: December 2020

A leptospirose é uma doença de distribuição cosmopolita, que apresenta incidência significativa, sendo, portanto, mais comum em países tropicais de clima quente e úmido. A doença é uma antropozoonose direta que acomete humanos, animais domésticos e selvagens. O gênero Leptospira é formado por espiroquetas que pertencem a ordem Spirochaetales, família Leptospiraceae são consideradas bactérias patogênicas, aeróbias obrigatórias, espiraladas, flexíveis e móveis. Nos equídeos, a leptospirose pode apresentar sintomatologia reprodutiva, ocular e respiratória, porém muitas vezes os animais são assintomáticos. Através de dados epidemiológicos e pela sintomatologia clínica, associando tais fatores aos testes laboratoriais, se pode chegar ao diagnóstico dessa enfermidade. Não existe um protocolo fixo para o tratamento da leptospirose em equídeos, porém, existe um consenso entre os clínicos e pesquisadores a respeito da utilização de antibióticos. O objetivo do trabalho foi realizar uma revisão de literatura, enfatizando os fatores de riscos associados a leptospirose em equídeos.
Lifestyle and Consumers’ Choice of Laundry Services in Manado City: An Empirical Study
Authors: Kaligis Jenny N.
Published: December 2020

Lifestyle, including activities, interests, and opinions, influences consumer shopping behaviour and decisions in laundry businesses. It relates to individual’s way of life, use of money and time, expressed in activities, interests, and opinions. Therefore, those partaking in this business need to understand their consumers’ lifestyle, for complete information on what products to offer, innovate, and promote. This study was conducted in Malalayang, Paal 2, Boulevard, and the areas surrounding Sam Ratulangi Airport, the central areas of modern laundry businesses in Manado. The findings suggest that lifestyle simultaneously conferred a positive and significant effect on consumer’s laundry service preference. Therefore, every related dimension, including activities, interests, and opinions, also significantly affects the parameter under investigation.