bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance comparison of aspirin assay between Anysis and VerifyNow: Assessment of therapeutic platelet inhibition in patients with cardiac diseases
Authors: Piao Jinxiang , Yoo Chaeyoung , Kim SeonYoung , Whang Youn-Wha , Choi Cheol Ung , Shin Sehyun
Published: May 2021

BACKGROUND: Assessment of platelet inhibition for aspirin therapy is important to manage patients who are at potential risk of developing thrombotic and hemorrhagic complications. OBJECTIVE: This study aimed to evaluate a new platelet assay (Anysis-aspirin), compare it with VerifyNow-aspirin in patients with cardiac diseases, and analyze the aspirin resistance rates between the two devices. METHODS: Citrated blood samples were collected from patients with cardiac diseases referred for the aspirin response test. In the Anysis assay, a test result was provided with a blood flow migration distance (MD) until blood flow stoppage, which was comparable to aspirin reaction units (ARUs) obtained using VerifyNow. The measurements were simultaneously conducted using the two devices and compared. RESULTS: The MD without and with aspirin use was 160±33 and 254±23 mm, respectively (p < 0.0001). Compared with VerifyNow (reference), the sensitivity and specificity of Anysis-200 were 96.3 and 90.3%, respectively (area under the curve, 0.968). Furthermore, the aspirin resistance rate in aspirin-administered patients was 20.9%using VerifyNow and 16.5%for Anysis-200. The Cohen’s kappa coefficient between the two devices was 0.81, indicating an almost perfect agreement between the two devices. CONCLUSIONS: Anysis-aspirin, a novel aspirin assay for assessing platelet inhibition, showed excellent agreement with VerifyNow-aspirin with high accuracy and precision. The Anysis-aspirin assay would be used as a point-of-care test to assess aspirin non-responsiveness and abnormal platelet reactivity.
Entorhinal Perfusion Predicts Future Memory Decline, Neurodegeneration, and White Matter Hyperintensity Progression in Older Adults
Authors: Bangen Katherine J. ,   for the Alzheimer’s Disease Neuroimaging Initiative, Thomas Kelsey R. , Sanchez Danielle L. , Edmonds Emily C. , Weigand Alexandra J. , Delano-Wood Lisa , Bondi Mark W.
Published: May 2021

Background: Altered cerebral blood flow (CBF) has been linked to increased risk for Alzheimer’s disease (AD). However, whether altered CBF contributes to AD risk by accelerating cognitive decline remains unclear. It also remains unclear whether reductions in CBF accelerate neurodegeneration and development of small vessel cerebrovascular disease. Objective: To examine associations between CBF and trajectories of memory performance, regional brain atrophy, and global white matter hyperintensity (WMH) volume. Method: 147 Alzheimer’s Disease Neuroimaging Initiative participants free of dementia underwent arterial spin labeling (ASL) magnetic resonance imaging (MRI) to measure CBF and serial neuropsychological and structural MRI examinations. Linear mixed effects models examined 5-year rate of change in memory and 4-year rate of change in regional brain atrophy and global WMH volumes as a function of baseline regional CBF. Entorhinal and hippocampal CBF were examined in separate models. Results: Adjusting for demographic characteristics, pulse pressure, apolipoprotein E ɛ4 positivity, cerebrospinal fluid p-tau/Aβ ratio, and neuronal metabolism (i.e., fluorodeoxyglucose standardized uptake value ratio), lower baseline entorhinal CBF predicted faster rates of decline in memory as well as faster entorhinal thinning and WMH progression. Hippocampal CBF did not predict cognitive or brain structure trajectories. Conclusion: Findings highlight the importance of early cerebrovascular dysfunction in AD risk and suggest that entorhinal CBF as measured by noninvasive ASL MRI is a useful biomarker predictive of future cognitive decline and of risk of both
Aging-Related Neural Disruption Might Predispose to Postoperative Cognitive Impairment Following Surgical Trauma
Authors: Ji Mu-huo , He Xue , Shen Jin-chun , Yang Jian-jun
Published: May 2021

Background: Accumulating evidence has demonstrated that aging is associated with an exaggerated response to surgical trauma together with cognitive impairments. This has significant implications for the development of clinical phenotype such as perioperative neurocognitive disorders (PND), which is a common complication following surgery, especially for the elderly. However, the mechanism by which aging brain is vulnerable to surgical trauma remains to be elucidated. Objective: To test whether age-related alterations in hippocampal network activities contribute to increased risk of PND following surgery. Methods: Thirty-two adult and seventy-two aged male C57BL/6 mice undergone sevoflurane anesthesia and exploratory laparotomy were used to mimic human abdominal surgery. For the interventional study, mice were treated with minocycline. Behavioral tests were performed post-surgery with open field, novel object recognition and fear conditioning tests, respectively. The brain tissues were then harvested and subjected to biochemistry studies. Local field potential (LFP) recording was performed in another separate experiment. Results: Aged mice displayed signs of neuroinflammation, as reflected by significantly increased proinflammatory mediators in the hippocampus. Also, aged mice displayed persistently decreased oscillation activities under different conditions, both before and after surgery. Further correlation analysis suggested that theta power was positively associated with time with novel object, while γ oscillation activity was positively associated with freezing time to context. Of note, downregulation of neuroinflammation by microglia inhibitor minocycline reversed some of these abnormities. Conclusion: Our study highlights that age-related hippocampal oscillation dysregulation increases the risk of PND incidence, which might provide diagnostic/prognostic biomarkers for PND and possible other neurodegenerative diseases.
Subtle Thyroid Dysfunction Is Not Associated with Cognitive Decline: Results from the ELSA-Brasil
Authors: Szlejf Claudia , Suemoto Claudia Kimie , Janovsky Carolina Castro Porto Silva , Bertola Laiss , Barreto Sandhi Maria , Lotufo Paulo Andrade , Benseñor Isabela Martins
Published: May 2021

Background: Subtle thyroid alterations have a controversial role in cognition. Objective: We investigated the longitudinal association of baseline thyroid function, thyrotropin (TSH), and thyroxine (FT4) levels with cognitive performance after 4 years of follow-up in middle-aged and older adults without overt thyroid dysfunction. Methods: We included 4,473 individuals, age≥55 years at the second study wave, without overt thyroid dysfunction at baseline. Individuals were divided according to thyroid function and TSH and FT4 tertiles. Cognition was assessed at baseline and after 4 years of follow-up by the word recall (DWR), semantic verbal fluency (SVF), and trail making (TMT) tests. The longitudinal association of thyroid function and TSH and FT4 tertiles with cognitive performance was investigated using generalized estimating equations adjusted for sociodemographic characteristics, lifestyle, cardiovascular risk factors and depression. Results: There was no longitudinal association of thyroid function and TSH and FT4 baseline levels with performance on the cognitive tests. However, there was a baseline cross-sectional U-shaped association of FT4 tertiles with poorer performance in the SVF (first FT4 tertile: β= –0.11, 95% CI = –0.17; –0.04; third FT4 tertile: β= –0.10, 95% CI = –0.17; –0.04) and of the third FT4 tertile with poorer performance in the DWR (β= –0.09, 95% CI = –0.16; –0.02). Conclusion: Thyroid function and hormone levels were not associated with cognitive decline during 4 years of follow-up in middle-aged and older adults without overt thyroid dysfunction. Future studies with longer follow-up could clarify the implications of subtle thyroid alterations in cognition.
The Interrelation Between Chronic Headache, Cognitive Scores, and MRI Markers Among Stroke Survivors
Authors: Molad Jeremy , Hallevi Hen , Korczyn Amos D. , Seyman Estelle , Bornstein Natan M. , Niry Dana , Eldor Roy , Ben Assayag Einor
Published: May 2021

Background: Migraine is known to mildly increase the risk for ischemic stroke and is associated with vascular MRI markers. However, the potential effect of chronic headache (CH) on stroke outcomes has not been studied. Objective: We aimed to assess the interrelation between CH and post-stroke cognitive impairment. Methods: Data from 455 patients with a first ever stroke from the TABASCO study was available. All patients underwent 3T brain MRI, blood analysis, and a serial cognitive assessment at baseline and 6, 12, and 24 months after. Results: Eighty-five (18.7%) patients reported suffering from CH, of whom 53 (62.4%) reported symptoms of photophobia or nausea, and 34 (40%) reported an aura. CH was associated with female sex, lower prevalence of T2DM (p < 0.001), and lower HbA1C levels (p < 0.001). Multiple regression analysis, controlling for age, sex, education, vascular risk factors, and the presence of acute lesions in MRI, revealed that CH was an independent predictor of better cognitive scores 6, 12, and 24 months post-stroke (p = 0.015, p = 0.01, and p = 0.012, respectively). Stroke patients suffering from CH had also higher normalized gray, white matter, and thalamus volumes, and better white matter microstructural integrity (p < 0.001, p = 0.037, p < 0.001, p = 0.008, respectively) Conclusion: In this study, CH was consistently associated with better long term cognitive scores among post stroke subjects. These surprising findings may partially arise from the higher prevalence of T2DM among subjects without CH, that may represent the existence of chronic cerebrovascular disease, and may reflect mechanisms involving glucose metabolism.
Blood DNA Methylation Levels in the WNT5A Gene Promoter Region: A Potential Biomarker for Agitation in Subjects with Dementia
Authors: Kobayashi Nobuyuki , Shinagawa Shunichiro , Nagata Tomoyuki , Tagai Kenji , Shimada Kazuya , Ishii Azusa , Oka Naomi , Shigeta Masahiro , Kondo Kazuhiro
Published: May 2021

Background: Behavioral and psychological symptoms of dementia (BPSD) cause a heavy burden for both patient and caregivers. These symptoms are diverse, and their mechanism is still unclear. Agitation is the most common and difficult to treat among BPSD. In recent years, while changes in DNA methylation levels have been receiving attention as a biomarker of aging and dementia, associations with BPSD have not been examined. Objective: Focusing on agitation, the objective of the present study was to identify a region where changes in DNA methylation levels are associated with agitation. Methods: Using genome-wide DNA methylation analysis data for 7 dementia subjects with agitation, 5 dementia subjects without agitation, and 4 normal elderly controls, we determined a signaling pathway in the WNT5A gene promoter region to be associated with agitation. Based on this result, we measured DNA methylation levels in this region for 26 dementia subjects with agitation and 82 dementia subjects without agitation by means of methylation-sensitive high-resolution melting (MS-HRM) analysis. Results: The WNT5A DNA methylation level in dementia subjects with agitation was significantly lower than in those without agitation (p = 0.001). Changes in WNT5A DNA methylation levels were not influenced by age, sex, body mass index, APOE ɛ4, medication, or inflammatory cytokines. Conclusion: Our results suggested an association of agitation with Wnt signaling, in particular with changes in WNT5A DNA methylation levels, which could be a potentially useful biomarker for predicting the appearance of agitation. It may contribute to the elucidation of the mechanism of BPSD.
Reliability of Telephone and Videoconference Methods of Cognitive Assessment in Older Adults with and without Dementia
Authors: Hunter Matthew B. , Jenkins Natalie , Dolan Clare , Pullen Hannah , Ritchie Craig , Muniz-Terrera Graciela
Published: May 2021

Background: Telephone and videoconference administration of cognitive tests introduce additional sources of variance compared to in-person testing. Reviews of test-retest reliability have included mixed neurocognitive and psychiatric populations with limited consideration of methodological and statistical contributions. Objective: We reviewed reliability estimates from comparison studies of older adults with and without dementia, considering test-retest analyses and study methods. Methods: Medline, Embase, PsycINFO, and Web of Science were systematically searched from 1 January 2000 to 9 June 2020 for original articles comparing telephone or videoconference administered cognitive instruments to in-person administration in older adults with and without dementia or mild cognitive impairment. Results: Of 4,125 articles, 23 were included: 11 telephone (N = 2 dementia cohorts) and 12 videoconference (N = 4 dementia cohorts). Telephone administered subtest scores trended in the same direction as in-person with comparable means. Person-level data were scarce. Data on dementia was only available for MMSE, with resulting subtle modality bias. MMSE, SMMSE, Letter Fluency, and HVLT-R in healthy to mild-moderate Alzheimer’s disease were particularly reliable for videoconference administration. Other tests show promise but require more observations and comprehensive analyses. Most studies used high-speed stable videoconferencing hardware resulting in a lack of ecological validity for home administration. Conclusion: Remote administration is often consistent with in-person administration but variable and limited at the person/test level. Improved statistical design and inclusion of dementia related cohorts in telephone studies is recommended. Reliability evidence is stronger for videoconferencing but with limited applicability to home administration and severe dementia. Improved reporting of administrative procedures is recommended.
Semi-analytical treatment of Hall current effect on peristaltic flow of Jeffery nanofluid
Authors: Eldabe Nabil Tawfik , Moatimid Galal M. , Abouzeid Mohamed , Elshekhipy Abdelhafeez A. , Abdallah Naglaa Fawzy
Published: May 2021

In the current paper, a semi-analytical solution is obtained for the problem of the peristaltic motion of Jeffrey nanofluid with heat transfer inside an asymmetric channel. The influences of variable viscosity, thermal conductivity, Hall currents, Joule heat and viscous dissipation are taken into consideration. The problem is modulated mathematically by a system of non-linear partial deferential equations which describe the fluid velocity, temperature and nanoparticles concentration distributions. This system is simplified by considering long wavelength of the peristaltic wave and low Reynolds number. It is solved by using the multi-step deferential transform method to obtain the velocity, temperature and nanoparticles concentration distributions, pressure rise and pressure gradient. These solutions are obtained as functions of the physical parameters of the problem. Tables and figures are discussed to see the effects of these parameters. It is found that the thermal conductivity parameter causes an increase in the pressure gradient, meanwhile it reduces the pressure rise. Furthermore, it is noticed that the behavior of Brownian and thermophoresis parameters on nanoparticles concentration field are quite reverse.
Emergency response model for intelligent logistics system based on resilience theory
Authors: Wang Long , Huang Mengqiu , Ming Shiqi , Yin Zongyi
Published: May 2021

The concept of intelligent logistics has become mature. However, most of the researches on risk management and emergency response for intelligent logistics system only focus on some specific systems, and ignore the discussions on the overall system. Thus, in order to establish emergency response model for intelligent logistics system, the accident management process of intelligent logistics system was analyzed, the accident management model of the system based on the resilience theory was designed, and finally a model of emergency response for intelligent logistics system from the perspective of dynamic accident management was developed. Results show that this model reflects the dynamic process of intelligent logistics system subjected to random accidental impacts, the in-depth learning of the intelligent logistics system for incident handling and the enhancement of the system’s risk resistance through feedback data on incident handling are reflected in the model. Conclusion obtained from this study provides a theoretical reference for the optimizing of researches on mechanism of emergency response.
Application of Bayesian network in the evaluation of adolescents’ language quality training
Authors: Jie Chen
Published: May 2021

At present, the imperfect network language environment has adversely affected to cultivate the adolescents’ language quality. Therefore, it is urgent to use modern information technology to design a new algorithm model for the cultivation of the adolescents’ language quality. Through in-depth research on the current research status of youth language quality training in recent years, it is found that the introduction of Bayesian network into the cultivation of adolescents’ language quality can effectively improve its training effect. A data mining algorithm and Bayesian network are used to design a BOPPPS model, with the model used to cultivate the language quality of adolescents. To verify whether the BOPPPS model can achieve good results in the cultivation of adolescents’ language quality, a simulation experiment on the BOPPPS model was conducted, evaluating the actual effect by using the multi-level fuzzy comprehensive evaluation model. Results show that the adolescents who are trained with the BOPPS model has significantly improved overall language quality before training, and thus it can be inferred that the BOPPPS model plays a positive role in improving the language quality of adolescents. The conclusions can provide some reference for the cultivation of adolescents’ language quality and has certain theoretical research significance.
Application of fingerprint encryption algorithm based on multi-feature extraction
Authors: Zheng Bing , Qiu Zhenyu , Yang Jing
Published: May 2021

The area of fingerprint image collected by mobile intelligent device is small, which will lead to the lack of extracted fingerprint feature information. In this study, a small area fingerprint encryption algorithm based on fingerprint mosaic was proposed. The experimental results on the real fingerprint database show that the proposed fingerprint mosaic method can significantly improve the performance of the small area fingerprint encryption algorithm, the acquisition failure rate decreased from 39% to 8%, and the correct acceptance rate increased from 48% to 69%. The algorithm uses fingerprint feature mosaic technology and fingerprint image mosaic technology to effectively increase fingerprint feature information.
Saccadic intrusion recognition intelligent algorithms based on different eye movement date classification methods
Authors: Jin Huibin , Ma Mingxia , Liu Haibo , Li Mengjie , Zhang Hong
Published: May 2021

The saccadic intrusion recognition algorithm proposed by Tokuda doesn’t take into account individual differences and also, since the accuracy is not high enough, three aspects in this regard have been vastly improved. Firstly, the algorithm is tailored to deal with three types of missing data such as low confidence, not on the screen, and time missing, which improves the data fault tolerance of the algorithm. Secondly, the improved algorithm refers to the E&K algorithm, utilizing the ratio of saccade speed to overall speed. Then, the adaptive speed threshold has been determined accurately, which improves the sensitivity of the algorithm. Finally, the algorithm adds the upper limit of the amplitude for identifying regular saccades and filters out rapid retrospectives with large amplitude. A combination of all three minor improvements boosts the overall accuracy of the algorithm. In addition, the N-back task experiment provided data, which has been processed by the improved algorithm, and aided in arriving at a conclusion consistent with the previous ones. The experimental data compares the effect of the improved algorithm and DBSCAN in identifying fixation points. Furthermore, the results demonstrate the improved algorithm being significantly better than the DBSCAN algorithm in regard to sequence and sensitivity of data points.
The KF-SVM-based fusion method for multi sensor uncertain system with correlated noise
Authors: Jiao Yuzhao , Lou Taishan , Wang Xiaolei , Zhao Hongmei
Published: May 2021

For multi-sensor target tracking system, the accurate state estimation is obtained under the condition that the system model is unbiased and the noise disturbance satisfies the characteristics of independent Gaussian white noise. However, in engineering practice, it is almost impossible to get the accurate system model and also the system noise is non-independent Gaussian white noise. So the traditional state estimation methods are not suitable for uncertainty system with non Gaussian white noise. In this paper, the Kalman Filter-Support Vector Machine (KF-SVM) based data fusion algorithm is proposed for system with model uncertainty and correlated noise. Firstly, the state pre-estimates are calculated by the proposed improved Kalman Filter for single sensor system. Then, the state estimation is obtained via proposed KF-SVM data fusion algorithm for multi-sensor system. Finally, compared with the traditional algorithms, the simulation results show that the proposed fusion algorithm based on KF-SVM does not need to calculate the sensor cross-covariance matrix and has better estimation accuracy.
An artificial intelligence-based real-time monitoring framework for time series
Authors: Sun Zhao , Peng Qinke , Mou Xu , Wang Ying , Han Tian
Published: May 2021

In the era of data technology, data growth is occurring at an unprecedented scale. Business data and information are among the most valuable assets. Massive data analysis now drives nearly every aspect of society and can facilitate informed decision-making by businesses. Fully automated data flow detection of anomalies plays a crucial role in maintaining data service stability and preventing malicious attacks. This paper presents an extensible and generic real-time monitoring system framework (EGRTMS) for large-scale time-series data. EGRTMS employs a prediction module and an anomaly detection module within an anomaly filtering layer for the accurate identification of anomalies. Moreover, the alarm module and anomaly handling module within an anomaly trace processing layer enables the system to respond swiftly to the detected threats. Our solution does not rely on the labelling of anomalies; instead, a predictor module with a deep learning attention-based mechanism learns the normal behaviour of the data, and an anomaly handling module determines the dynamic alarm-threshold by utilizing a sliding window. The results of this study demonstrate that our framework significantly outperforms other anomaly detection systems on most real and synthetic datasets.
Extended failure mode and effect analysis approach based on hesitant fuzzy linguistic Z-numbers for risk prioritisation of nuclear power equipment failures
Authors: Peng Heng-ming , Wang Xiao-kang , Wang Tie-li , Liu Ya-hua , Wang Jian-qiang
Published: May 2021

The successful diagnosis of nuclear power equipment failures plays a vital role in guaranteeing the safe operation of nuclear power systems. Failure mode and effect analysis (FMEA) is one of the most commonly used methods for identifying potential failures. However, several shortcomings associated with the conventional FMEA method limit its further application. This paper develops an extended FMEA approach based on hesitant fuzzy linguistic Z-numbers (HFLZNs). Firstly, the concept of HFLZNs is proposed to describe the evaluation information, which inherits the prominent features of the hesitant fuzzy linguistic term set and linguistic Z-numbers (LZNs). Secondly, an HFLZN assessment method is developed to determine the weights of risk factors, and the weights of experts are measured based on hesitation degree. Subsequently, considering the psychological characteristics of decision makers, Tomada de Decisão Iterativa Multicritério and LZNs are integrated to obtain the risk ranking of failure modes. Finally, the practicability of the extended FMEA method is proven by an illustrative example concerning the risk evaluation of a nuclear main pump bearing, and its robustness is verified by indepth analysis.
Two forms of three-way semiconcepts
Authors: Mao Hua , Liu Xiaoqing , Wang Gang
Published: May 2021

Semiconcept, a new data processing model, enriches the development of formal concept analysis. However, the classical semiconcept is supported by two-way decisions. In the study of classical semiconcept theory, how to express the information of jointly not possessed is also essential in making decisions. Therefore, this paper tries to combine the classical semiconcept theory with three-way decisions to present three-way semiconcepts, and carry on the further study. Firstly, we define new operators and give some properties of them. Two kinds of three-way semiconcepts —OE-semiconcept and AE-semiconcept, are presented. And the corresponding structures are searched out from the perspective of lattice theory. Furthermore, we analyze the relationship among three-way concepts, three-way semiconcepts and classical semiconcepts. On this basis, the algorithms to build OE-semiconcept and AE-semiconcept are presented. At the meanwhile, we take some examples to examine and explain the obtained results.
Continuous and bounded linear operators in neutrosophic normed spaces
Authors: Khan Vakeel A. , Esi Ayhan , Ahmad Mobeen , Daud Khan Mohammad
Published: May 2021

In this article, we show that the addition and scalar multiplication in neutrosophic normed spaces are continuous. The neutrosophic boundedness and continuity of linear operators between neutrosophic normed spaces are examined. Moreover, we analyzed that the set of all neutrosophic continuous linear operators and the set of all neutrosophic bounded linear operators from neutrosophic normed spaces into another are vector spaces.
On lacunary generalized statistical convergent complex uncertain triple sequence
Authors: Demirci Işıl Açık , Gürdal Mehmet
Published: May 2021

In this work, we study the lacunary I -statistical convergence concept of complex uncertain triple sequence. Four types of lacunary I -statistically convergent complex uncertain triple sequences are presented, namely lacunary I -statistical convergence in measure, in mean, in distribution and with respect to almost surely, and some basic properties are proved.
ELF-Net: Enriching local features network for 3D point cloud classification and semantic segmentation
Authors: Chen Lifang , Wei Mengru
Published: May 2021

With the popularity of depth sensors and 3D scanners, 3D point cloud has developed rapidly. 3D scene understanding based on deep learning has become a research hotspot. However, many existing networks failed to fully consider the local structures of point clouds, limiting their abilities to exploit the complicated relationships between points. In this paper, we propose Enriching Local Features Network (ELF-Net), which enriches local features of point clouds. We propose Local Points Encoding Module (LPEM) and Feature Concatenate Module (FCM) in our network. Specifically, LPEM is designed to encode the information of eight orientations and 3D coordinate information of local points. We stack the encoding units to achieve multi-scale representation, which is conducive to obtaining robustness and capturing details of the network. In Set Abstraction (SA) module, we apply farthest point sampling (FPS) method to sample the initial points and ball query method is used to group the neighboring points within a radius. FCM is designed to update the representations of local points by applying graph attention mechanism in local regions, which aims to enrich neighboring point feature representations. Finally, our network also proposes a new multivariate loss function, which combines the Center Loss function and Cross Entropy loss function to act on the classification branch. Experimental results show the effectiveness of our proposed network on ModelNet40 (achieves 92.35% accuracy), ScanNet (achieves 85.46% accuracy) and S3DIS (achieves 86.4% accuracy) datasets.
SQLSketch: Generating SQL Queries using a sketch-based approach
Authors: Ahkouk Karam , Machkour Mustapha , Majhadi Khadija , Mama Rachid
Published: May 2021

In the last decade, many intelligent interfaces and layers have been suggested to allow the use of relational databases and extraction of the content using only the natural language. However most of them struggle when exposed to new databases. In this article, we present SQLSketch, a sketch-based network for generating SQL queries to address the problem of automatically translate Natural Languages questions to SQL using the related databases schemas. We argue that the previous models that use full or partial sequence-to-sequence structure in the decoding phase can, in fact, have counter-effect on the generation operation and came up with more loss of the context or the meaning of the user question. In this regard, we use a full sketch-based structure that decouples the generation process into many small prediction modules. The SQLSketch is evaluated against GreatSQL, a new cross-domain, large-scale and balanced dataset for the Natural Language to SQL translation task. For a long-term aim of making better models and contributing in adding more improvements to the semantic parsing tasks, we propose the GreatSQL dataset as the first balanced cross-domain corpus that includes 45,969 pairs of natural language questions and their corresponding SQL queries in addition to simplified and well structured ground-truth annotations. We establish results for SQLSketch using GreatSQL dataset and compare the performance against two popular types of models that represent the sequential and partial-sketch based approaches. Experimental result shows that SQLSketch outperforms the baseline models by 13% in exact matching accuracy and achieve a score of 23.9% to be the new state-of-the-art model on GreatSQL.
MF-COTE: A chinese opinion target extraction model based on multiple features
Authors: Dang Xingyue , Liao Shan , Cheng Pengsen , Liu Jiayong
Published: May 2021

Recently, deep learning methods have been applied to deal with the opinion target extraction (OTE) task with fruitful achievements. On the other hand, since the features captured by the embedding layer can make a multiple-perspective analysis from a sentence, an embedding layer that can grasp the high-level semantics of the sentences is of essence for processing the OTE task and can improve the performance of model into a more efficient manner. However, most of the existing studies focused on the network structure rather than the significant embedded layer, which may be the fundamental reason for the problem of relatively poor performance in this field, not mention the Chinese extraction model. To compensate these shortcomings, this paper proposes a model using multiple effective features and Bidirectional Encoder Representations from Transformers (BERT) on the architecture of Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Field (CRF) for Chinese opinion target extraction task, namely MF-COTE, which can construct features from different perspectives to capture the context and local features of the sentences. Besides, to handle the difficult case of multiple nouns in one sentence, we innovatively propose noting words feature to regulate the model emphasize on the noun near the transition or contrast word, thus leading a better opinion target location. Moreover, to demonstrate the superiorities of the proposed model, extensive comparison experiments are systematically conducted compared with other existing state-of-the-art methods, with the F1-score of 90.76%, 92.10%, 89.63% on the Baidu, the Dianping, and the Mafengwo dataset, respectively.
Tauberian theorems for statistical Cesàro and statistical logarithmic summability of sequences in intuitionistic fuzzy normed spaces
Authors: Yavuz Enes
Published: May 2021

We define statistical Cesàro and statistical logarithmic summability methods of sequences in intuitionistic fuzzy normed spaces(IFNS) and give slowly oscillating type and Hardy type Tauberian conditions under which statistical Cesàro summability and statistical logarithmic summability imply convergence in IFNS. Besides, we obtain analogous results for the higher order summability methods as corollaries. Also, two theorems concerning the convergence of statistically convergent sequences in IFNS are proved in the paper.
Six-Month Community-Based Brisk Walking and Balance Exercise Alleviates Motor Symptoms and Promotes Functions in People with Parkinson’s Disease: A Randomized Controlled Trial
Authors: Mak Margaret K.Y. , Wong-Yu Irene S.K.
Published: May 2021

Background: In Parkinson’s disease (PD), sustained aerobic exercise is a promising therapy in delaying motor disability. Brisk walking is a moderate intensity aerobic training, which could be translated to community practice at low cost, but its effects on motor symptoms remains unclear. Objective: To determine the effectiveness of a six-month brisk walking and balance program in alleviating motor symptoms, and promoting functional, gait, and balance performance in people with PD. Methods: Seventy individuals with mild to moderate PD were randomly assigned to a brisk walking (BW) group or an active control (CON) group. BW group received ten 90-minute supervised brisk walking and balance exercise for six months (weeks 1–6: once/week, weeks 7–26: once/month). CON group received upper limb training. Both groups performed 2-3 self-practice sessions weekly. Primary outcome was Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) motor score. Secondary outcomes were fast gait speed (FGS), timed-up-and-go (TUG) time, six-minute walk distance (6MWD), and Mini-Balance Evaluation Systems Test (Mini-BEST) score. Results: Sixty-four participants (33 BW/31 CON) completed training. BW group showed greater significant decreases from baseline than CON group in MDS-UPDRS motor score after six weeks (–5.5 vs –1.6, p < 0.001) and 6 months (–6.0 vs –1.4, p < 0.001) of training. BW group also showed greater significant improvement from the baseline than CON group for TUG time, FGS, 6MWD, and mini-BEST score (all p < 0.05). Conclusion: The six-month brisk walking and balance program alleviates motor symptoms, promotes functional and gait performance, walking capacity, and dynamic balance in people with mild to moderate PD.
Characterization of Depressive and Anxiety Symptoms in Idiopathic REM Sleep Behavior Disorder
Authors: Honeycutt Lucy , Gagnon Jean-François , Pelletier Amélie , Montplaisir Jacques Y. , Gagnon Geneviève , Postuma Ronald B.
Published: May 2021

Background: Depression and anxiety are common in synucleinopathies and often present during prodromal stages, including idiopathic/isolated REM sleep behavior disorder (iRBD). However, the specific profiles of depression/anxiety and their predictive values for phenoconversion remain unclear. Objective: To assess the predominant manifestations, predictive value, and changes over time in depressive and anxiety symptoms in iRBD. Methods: Patients with polysomnography-confirmed iRBD (n = 114) and healthy controls (n = 44) were recruited. The Beck Depression Inventory and Beck Anxiety Inventory were administered at baseline, which was repeated prospectively over follow-up. Factor solutions were generated to delineate symptom clusters within the scales, and to help disentangle primary mood symptoms from other neurodegenerative confounds. Total scores, individual scale items, and factors were evaluated to 1) compare patients and controls, 2) assess progression of symptoms over time, and 3) assess predictive value for phenoconversion. Results: At baseline, iRBD patients had more severe depressive (9.0 = 6.7 vs 5.8 = 4.8) and anxiety (7.0 = 7.9 vs 4.5 = 6.0) symptoms than controls. Increased scores were seen in numerous individual scale items and most scales’ factors. For depressive symptoms, there was no progression of total scores or factors over time. However, anxiety scores worsened slightly over prospective follow-up (annual slope = 0.58 points, p < 0.05). Over an average 2.4 = 3.1-year follow-up, 37 patients phenoconverted and 72 remained disease-free. Neither baseline depressive nor anxiety symptoms predicted phenoconversion to clinical neurodegenerative disease. Conclusions: Depressive and anxiety symptoms are common in iRBD. However, they do not predict phenoconversion and show only modest progression over time, solely for anxiety.
Effects of respiratory muscle training on cough function in neurological disorders: A systematic review with meta-analysis
Authors: He Yi , Zhao Chen , Liu Yong
Published: May 2021

BACKGROUND: Patients with neurological disorders can present the weakness of respiratory muscle and impaired cough function. Previous studies have shown that respiratory muscle strength training (RMT) is an effective method of improving the strength of respiratory muscle. The effects of RMT on cough function remain controversial. OBJECTIVE: We aimed to analyze randomized controlled trials (RCTs) that investigated the effects of RMT on cough function of patients with neurological disorders. METHODS: Pubmed, Medline, Embase, and the Cochrane Library were searched electronically for RCTs. Two reviewers independently performed data extraction and quality assessment. Data were analyzed by using RevMan 5.3 software of The Cochrane Collaboration. RESULTS: Five studies with 185 participants were included. The mean PEDro score was 6.2 (range 5 to 7), showing moderate methodological quality. Random-effects meta-analyses showed that respiratory muscle training improved peak expiratory cough flow of voluntary cough by 2.16 (95% CI 1.16 to 3.17) and involuntary cough by 2.84 (95% CI 1.29 to 4.39), with statistical significance (P < 0.0001, P = 0.0003). The experimental group had an improvement of 0.19 cmH2O (95% CI –0.12 to 0.5) on the maximal inspiratory pressure, 0.09 cmH2O (95% CI –0.23 to 0.42) on the maximal expiratory pressure, but with no statistical significance (P = 0.23, P = 0.58) between groups. CONCLUSION: Respiratory muscle training was considered as an effective method for improving cough function. However, this review was insufficient to conclude whether respiratory muscle training was effective in improving inspiratory and expiratory muscle strength, this was opposite with previous meta-analysis. These effects might due to the small samples and different diseases.
High-level ETL for semantic data warehouses
Authors: Deb Nath Rudra Pratap , Romero Oscar , Pedersen Torben Bach , Hose Katja
Published: May 2021

The popularity of the Semantic Web (SW) encourages organizations to organize and publish semantic data using the RDF model. This growth poses new requirements to Business Intelligence technologies to enable On-Line Analytical Processing (OLAP)-like analysis over semantic data. The incorporation of semantic data into a Data Warehouse (DW) is not supported by the traditional Extract-Transform-Load (ETL) tools because they do not consider semantic issues in the integration process. In this paper, we propose a layer-based integration process and a set of high-level RDF-based ETL constructs required to define, map, extract, process, transform, integrate, update, and load (multidimensional) semantic data. Different to other ETL tools, we automate the ETL data flows by creating metadata at the schema level. Therefore, it relieves ETL developers from the burden of manual mapping at the ETL operation level. We create a prototype, named Semantic ETL Construct (SETLCONSTRUCT), based on the innovative ETL constructs proposed here. To evaluate SETLCONSTRUCT, we create a multidimensional semantic DW by integrating a Danish Business dataset and an EU Subsidy dataset using it and compare it with the previous programmable framework SETLPROG in terms of productivity, development time, and performance. The evaluation shows that 1) SETLCONSTRUCT uses 92% fewer Number of Typed Characters (NOTC) than SETLPROG, and SETLAUTO (the extension of SETLCONSTRUCT for generating ETL execution flows automatically) further reduces the Number of Used Concepts (NOUC) by another 25%; 2) using SETLCONSTRUCT, the development time is almost cut in half compared to SETLPROG, and is cut by another 27% using SETLAUTO; and 3) SETLCONSTRUCT is scalable and has similar performance compared to SETLPROG. We also evaluate our approach qualitatively by interviewing two ETL experts.
Comparison of efficacy of vestibular rehabilitation and noisy galvanic vestibular stimulation to improve dizziness and balance in patients with multiple sclerosis
Authors: Lotfi Yones , Farahani Akram , Azimiyan Mojtaba , Moossavi Abdollah , Bakhshi Enayatollah
Published: May 2021

BACKGROUND: Dizziness and imbalance are common symptoms in patients with multiple sclerosis (PwMS), and rehabilitation interventions varying greatly in effectiveness. OBJECTIVE: To compare the effectiveness of vestibular rehabilitation therapy (VRT) and noisy galvanic vestibular stimulation (nGVS) on dizziness and balance in PwMS. METHODS: This was a single-blind, randomized controlled trial. Twenty-four PwMS were randomly divided into groups of VRT, nGVS, and Control. The VRT and the nGVS groups underwent the intervention program. The patients were assessed with the composite score in anteroposterior and lateral directions (CS AP and LAT) obtained by sensory organization test (SOT), Dizziness Handicap Inventory (DHI), and Activities-Specific Balance Confidence Scale (ABC). RESULTS: The VRT group showed greater improvements in CS AP and LAT, DHI total score, and ABC total score compared with the nGVS group and the control group. No significant difference was found between the nGVS group and the control group. These results were approximately stable at the 4-week follow-up. CONCLUSIONS: These findings provided evidence for effectiveness of the VRT in improvement of dizziness and balance in PwMS. These improvements were not associated with the nGVS. Further studies are needed to assess the effectiveness of the nGVS on dizziness and balance in PwMS.
Comparison of irregular flux viewer system with BONENAVI version for identification of Tc-99m MDP whole body bone scan metastasis images
Authors: Yu Chang-Ching , Ting Chien-Yi , Yang Ming-Hui , Chan Hung-Pin
Published: May 2021

The Tc-99m methylene diphosphonate (MDP) whole body bone scan (WBBS) has been widely accepted as a method of choice for the initial diagnosis of bone and joint changes in patients with oncologic diseases. The WBBS has shown high sensitivity but relatively low specificity due to bone variation. This study aims to use the self-developing irregular flux viewer (IFV) system to predict possible bone lesions in planar WBBS. The study uses gradient vector flow (GVF) and self-organizing map (SOM) methods to analyze the blood fluid-dynamics and evaluate hot points. The evaluation includes a selection of 368 patients with bone metastasis from prostate cancer, lung cancer and breast cancer. Finally, we compare IFV values with BONENAVI version data. BONENAVI is a computer-assisted diagnosis system that analyzes bone scintigraphy automatically. The analysis shows that the IFV system achieves sensitivities of 93% for prostate cancer, 91% for breast cancer, and 83% for lung cancer, respectively. On the other hand, our proposed approach achieves a higher sensitivity than the results of BONEVAVI version 2.0.5 for prostate cancer (88%), breast cancer (86%) and lung cancer (82%), respectively. The study results demonstrate that the high sensitivity and specificity of the IFV system can provide assistance for image interpretation and generate prediction values for WBBS.
PENGARUH LINGKUNGAN KERJA DAN KEPEMIMPINAN TERHADAP KINERJA KARYAWAN PADA PT. UNIFORM INDONESIA DI JAKARTA
Authors: Wilandari Devi Fitria
Published: May 2021

Penelitian ini bertujuan untuk mengetahui pengaruh lingkungan kerja dan kepemimpinan terhadap kinerja karyawan pada PT. Uniform Indonesia di Jakarta. Metode yang digunakan adalah explanatory research dengan teknik analisis menggunakan analisis statistik dengan pengujian regresi, korelasi, determinasi dan uji hipotesis. Hasil penelitian ini lingkungan kerja berpengaruh signifikan terhadap kinerja karyawan sebesar 43,6%, uji hipotesis diperoleh t hitung > t tabel atau (8,276 > 2,008). Kepemimpinan berpengaruh signifikan terhadap kinerja karyawan sebesar 46,8%, uji  hipotesis diperoleh t hitung > t tabel atau (6,109 > 2,008). Lingkungan kerja dan kepemimpinan secara simultan berpengaruh signifikan terhadap kinerja karyawan dengan persamaan regresi Y = 10,013 + 0,285X1 + 0,325X2 dan kontribusi pengaruh sebesar 56,0%, uji hipotesis diperoleh F hitung > F tabel atau (31,861 > 2,790).
Technology for building ontologies for an information system to support scientific and educational activities
Authors:   L.N. Gumilyov Eurasian National University, Sadirmekova Zh.B. , Tussupov J.A. ,   L.N. Gumilyov Eurasian National University, Sambetbayeva M.A. ,   L.N. Gumilyov Eurasian National University,   Institute of Information and Computing Technologies, Altynbekova Zh.T. ,   Taraz Innovative Humanitarian University
Published: May 2021

The article presents an approach to organizing scientific portals based on ontologies. Ontology is the information basis of the Internet portal of knowledge, which should provide integration and systematization of scientific knowledge and information resources of a certain subject, as well as meaningful access to them from any "point" of Internet space. The ontology automatically builds a diagram of the portal's internal database and forms for filling it out, organizes navigation through the portal's information space, and ensures that search queries are formulated in terms of the knowledge portal's subject area. The division of the portal's ontology into subject-independent and subject-specific ontologies makes the portal customizable for almost any field of scientific knowledge. This technology allows declarative adjustment of the ontology during the operation of the knowledge portal, which allows you to track the dynamics of the emergence of new knowledge and information resources on the subject of the portal and thus provides support for its relevance and usefulness.
STUDI KEANEKARAGAMAN TUMBUHAN OBAT YANG DIGUNAKAN DALAM PENGOBATAN TRADISIONAL MASYARAKAT KABUPATEN PAMEKASAN
Authors: Gita Rina Sugiarti Dwi , Danuji Sarwo
Published: May 2021

Jamu tradisional di Pulau Madura dibuat dari racikan bahan tetumbuhan dan telah menjadi warisan keluarga secara turun-temurun. Warisan secara turun temurun ini perlu terus dipertahankan, dilestarikan dan perlu ditingkatkan kualitasnya. Tujuan dari penelitian ini adalah mengeksplorasi tumbuhan obat yang digunakan untuk pengobatan tradisional khususnya kabupaten Pamekasan. Metode Penelitian dilaksanakan secara random sampling pada 5 kecamatan dari keseluruhan 13 kecamatan dalam lingkup kabupaten Pamekasan. Pengumpulan data menggunakan metode survey eksploratif yaitu wawancara dan pengamatan langsung di lapangan, kemudian dilanjutkan dengan analisis serta pengolahan data. Disimpulkan bahwa terdapat 13 jenis tumbuhan obat tradisional di Kabupaten Pamekasan yang dimanfaatkan oleh masyarakat secara tradisional berdasarkan kebiasaan dan pengalaman secara turun temurun. Bagian tumbuhan yang dimanfaatkan sebagai obat ialah daun, akar, batang, bunga, buah, dan biji. Teknik meramu tumbuhan obat oleh masyarakat di Kabupaten Pamekasan yaitu dengan cara diseduh, direbus, diperas, dikunyah, dan dicampur dalam masakan.