bluuebunny/crossref_metadata_2025 huggingface dataset:
FOXD3-AS1 suppresses the progression of non-small cell lung cancer by regulating miR-150/SRCIN1axis
Authors: Ji Tao , Zhang Yanan , Wang Zheng , Hou Zuoxu , Gao Xuhui , Zhang Xiaoming
Published: September 2020

BACKGROUND: Long non-coding RNA (lncNRA) forkhead box D3 antisense RNA 1 (FOXD3-AS1) has been proved to promote or suppress the occurrence and development of multiple types of human tumors. However, the function and mechanism of FOXD3-AS1 in non-small cell lung cancer (NSCLC) are scarcely understood. METHODS: qRT-PCR was used for detecting FOXD3-AS1, miR-150 and SRC kinase signaling inhibitor 1 (SRCIN1) mRNA expression in NSCLC tissues, and the relationship between pathological characteristics of NSCLC patients and FOXD3-AS1 expression level was analyzed. With human NSCLC cell lines H1299 and A549 as cell models, CCK-8 and BrdU assays were employed for detecting cancer cell proliferation, and Transwell assay was employed for detecting cell invasion ability. Dual luciferase reporter gene assay and RNA immunoprecipitation (RIP) assay were used for the verification of the targeting relationshipe between FOXD3-AS1 and miR-150, and Western blot was employed for detecting SRCIN1 protein expression. RESULTS: FOXD3-AS1 expression was significantly reduced in NSCLC tissues and cell lines, and low expression of FOXD3-AS1 was closely related to positive lymph node metastasis and relatively high tumor grade. FOXD3-AS1 over-expression inhibited the proliferation and invasion of H1299 cell lines, while its knockdown promoted the proliferation and invasion of A549 cells. Additionally, it was confirmed that FOXD3-AS1 suppressed the expression of miR-150 by targeting it, and up-regulated the expression of SRCIN1. CONCLUSIONS: FOXD3-AS1 indirectly enhances the expression of SRCIN1 by targeting miR-150, thereby inhibiting NSCLC progression.
Ultrasound elastography for the detection of capsular fibrosis in breast implants: First results
Authors: Jung Ernst , Hösl Vanessa , von Fraunberg Sarah , Jung Friedrich , Prantl Lukas
Published: September 2020

BACKGROUND: Capsular contractures around breast implants usually develop leading to pain and aesthetically inadequate results and ultimately often requires the replacement of the implants. Textured silicone implants are the most commonly placed implant, but polyurethane-coated implants are increasingly being used in an attempt to ameliorate the long-term complications associated with implant insertion. AIM: Capsular contracture is traditionally classified using the Baker scale, a subjective classification system based upon clinical findings. Aim of this study was to evaluate the association between pain due capsular contraction, Baker score and different techniques of US elastography. MATERIAL AND METHODS: Patients were contacted who had undergone an implant replacement due to capsular contracture. Inclusion criterion was the re-implantation of a PU-coated implant. In the third year after changing the implant a follow-up examination was performed in 16 patients with 23 implants. A conventional examination with anamnesis, tactile and visual findings to obtain a Baker score, and ultrasound examinations including shear wave elastography, ARFI and compound elastography were performed. In addition, pain was evaluated using a visual analogue scale (VAS). RESULTS: The pain data showed a significant improvement (before implant exchange: 4.1±2.8 score points) with significance in favor of the current state (1.7±1.0 pain score points; p = 0.002). All patients suffered from less or no pain three years after exchange of the implant. Pain values and elastography (ARFI values) correlated well (r = 0,873), with increasing Baker score the ARFI values increased. US elastography evaluations can locally determine tissue density but correlate only to a limited extent with the test findings according to Baker. US elastography values of mammary gland tissue without implant did not differ from mammary gland tissue around implants. CONCLUSION: Preoperative Baker scores prior to exchange and the current Baker scores at the follow-up showed significantly lower score points three years after exchange of the implants. Ultrasound elastography seems to be an objective classification of capsular fibrosis. These first results motivate to initiate a prospective multicenter investigation.
Doppler ultrasonography and contrast-enhanced ultrasonography to evaluate liver allograft discard: A pilot prospective study
Authors: Zhang Hong-jun , Zheng Bo-wen , Gu Shi-jie , Wu Tao , Wu Li-li , Lian Yu-fan , Tong Ge , Yi Shu-hong , Ren Jie
Published: September 2020

BACKGROUND: Broad hemodynamic changes, is believed to have a profoundly damaging effect on donor livers after brain death (DBD) or cardiac death (DCD). It remains unclear whether Doppler ultrasonography (DUS) and contrast-enhanced ultrasonography (CEUS), the imaging modalities to evaluate perfusion, could provide more information of liver discarded. OBJECTIVE: To evaluate the ability of DUS and CEUS to predict the risk of DBD or DCD liver discarded. METHODS: The consecutive DBD or DCD donors with DUS/CEUS examinations before surgical procurement from February 2016 to June 2018 at our institution were included. The US and CEUS images of each donor liver were analyzed and the parameters were recorded. RESULTS: Among the 67 eligible donor livers, 15 (22.4%) were discarded and 52 (77.6%) were used. The discarded livers showed prolonged SAT of hepatic artery (0.08s vs 0.06s, OR = 2.169, P = 0.008) on DUS, less cases with homogeneous enhancement (40.0% vs 73.1%, OR = 0.243, P = 0.028) on CEUS, more cases with decreased enhancement (53.3% vs 19.2%, OR = 4.800, P = 0.009), and less difference of the peak time between portal vein and liver parenchymal (0.5s vs 6.7s, OR = 0.917, P = 0.034). The multivariable analysis showed that donor liver with prolonged SAT of hepatic artery (OR = 7.304, 95% CI: 1.195–44.655, P = 0.031) and decreased enhancement (OR = 2.588, 95% CI: 1.234–5.426, P = 0.012) were independent factors of liver discarded. CONCLUSIONS: DUS/CEUS could be applied as a promising predictive tool to screen high-risk liver donors. The prolonged SAT of hepatic artery on DUS and the decrease of liver donor in enhancement on CEUS, indicating hemodynamic changes in DBD and DCD donor livers, were risk factors of liver discarded.
Contrast-enhanced ultrasound (CEUS) in the diagnostic evaluation of popliteal artery aneurysms, a single-center study
Authors: Schwarze V. , Marschner C. , de Figueiredo G. Negrão , Rübenthaler J. , Clevert D.-A.
Published: September 2020

BACKGROUND: Popliteal artery aneurysms (PAA) are the most common peripheral artery aneurysms. Most common cause is arteriosclerosis. Acute thromboembolic limb ischemia and rupture of the PAA depict severe complications. Diagnostic tools for identifying PAAs are (Doppler) ultrasound, CT/MR angiography and DSA. PURPOSE: The aim of the present retrospective single-center study is to assess the application and safety of CEUS for assessing untreated and treated PAAs. MATERIALS AND METHODS: 13 patients were included in this study on whom CEUS was performed between 2007–2016. CEUS examinations were performed and interpreted by an experienced single radiologist (EFSUMB Level 3). RESULTS: CEUS allowed for the detection of PAAs in all cases. CEUS allowed for detection of partial thrombosis of PAA in 7/8 of untreated patients, proper exclusion of PAA upon femoro-popliteal bypass in 3 patients, incomplete exclusion of PAA upon femoro-popliteal bypass in 1 patient and ruling out of in-stent stenosis in 1 patient. CONCLUSION: CEUS is a useful and safe tool for in real-time evaluation of PAAs in the pre-/post-treatment status. In addition to conventional (Doppler) ultrasound and as an alternative tool to more elaborate imaging modalities, CEUS might be integrated in the future diagnostic work-up and follow-up of PAA patients.
A learning-by-doing based framework for micro-knowledge management in project contexts
Authors: Caramidaru Ibrian , Ionica Andreea , Leba Monica
Published: September 2020

BACKGROUND: There is a general acknowledgement of projects as sites of knowledge creation, but in multi-project contexts, we witness at times a lack of robust procedures that would ensure harvesting the lessons learned. OBJECTIVE: This paper aims at providing an operational conceptual framework for micro-knowledge management, depicting knowledge as created by individuals performing project activities. METHODS: Documentation on relevant literature and observation of current managerial practices and tools, in order to use the underpinnings of experiential education for managing learning-by-doing knowledge creation. RESULTS: Developing the framework led to identifying the decision-making processes in micro-knowledge management and their correlation with human resources management Secondly - we have shown the need for a coherent integration of knowledge deliverables into the project management tools and practices, especially in the context of the COVID-19 pandemic. CONCLUSIONS: Micro-knowledge management points to a recurrent assessment of the opportunity for micro-knowledge codification and/or staff retention based on tacit professional or biographical knowledge of the project team members. The framework offers a balance between the detachment of knowledge from the owners, in codification, and affirming the irreducible tacit and personal dimensions of learning-by-doing.
Caregivers’ Sense of Coherence: Implications on Direct and Indirect Costs of Dementia Care
Authors: Turró-Garriga Oriol , Viñas-Díez Vanesa , Conde-Sala Josep Lluís , Calvó-Perxas Laia , Cullell-Juncà Marta , Mas-Vall-llosera Glòria , Flaqué Margarida , Turon-Estrada Antoni , Juvinyà-Canal Dolors , Mioshi Eneida , Garre-Olmo Josep
Published: September 2020

Background: Dementia care is associated with physical, emotional, and monetary impact on the informal carers providing unpaid care. Differences in the personal characteristics of caregivers may help explain the variations in the costs of dementia care. Objective: The aim of this study was to analyze the effect of caregivers’ sense of coherence (SOC) on direct and indirect costs in dementia care. Methods: A cross-sectional study was conducted in community dwelling caregivers of patients with Alzheimer’s disease. Data of healthcare services were obtained from clinical registries, and information was collected from caregivers regarding their use of social care resources and time spent caregiving. The transformation of all costs into Euros was made assigning a fixed cost of 10.29 € /h and 16.24 € /h for assisting in instrumental and basic activities of daily living, respectively. Caregivers’ SOC was assessed using the Orientation to Life Questionnaire (OLQ-13). Adjusted regression models were developed, with different types of costs as dependent variables. Results: A sample of 147 caregivers was recruited. The mean OLQ-13 score was 73.3 points (SD = 11.6). The regression models showed a small association between caregivers’ SOC and direct costs, mainly linked to the use of social care resources (r2 = 0.429; β= –15.6 € /month), and a greater association between SOC and indirect costs (r2 = 0.562; β= –222.3 € /month). Conclusion: Increasing caregivers’ SOC could reduce dementia care costs by decreasing the use of social care resources and caregiving time.
Posterior Cingulate Cortex Hypometabolism in Non-Amnestic Variants of Alzheimer’s Disease
Authors: Bergeron David , Beauregard Jean-Mathieu , Jean-Guimond  , Soucy Jean-Paul , Verret Louis , Poulin Stéphane , Matias-Guiu Jordi A. , Cabrera-Martín María Nieves , Bouchard Rémi W. , Laforce Robert
Published: September 2020

Background: Hypometabolism of the posterior cingulate cortex (PCC) is an important diagnostic feature of late-onset, amnestic Alzheimer’s disease (AD) measured with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). However, it is unclear whether PCC hypometabolism has diagnostic value in young-onset, non-amnestic variants of AD, which exhibit less pathology in the hippocampus and default mode network. Objective: Evaluate the prevalence and diagnostic value of PCC hypometabolism in non-amnestic variants of AD. Methods: We retrospectively identified 60 patients with young-onset, atypical dementia who have undergone a detailed clinical evaluation, FDG-PET, and an amyloid biomarker (amyloid-PET or cerebrospinal fluid analysis). We quantitatively analyzed regional hypometabolism in 70 regions of interest (ROI) using the MIMneuro® software. Results: Based on a cut-off of z-score < –1.5 for significant PCC hypometabolism, the prevalence of PCC hypometabolism in non-amnestic variants of AD was 65% compared to 28% in clinical variants of frontotemporal dementia (FTD). The ROI with the maximal hypometabolism was the dominant middle temporal gyrus in the language variant of AD (mean z score –2.28), middle occipital gyrus in PCA (–3.24), middle temporal gyrus in frontal AD (–2.70), and angular gyrus in corticobasal syndrome due to AD (–2.31). The PCC was not among the 10 most discriminant regions between non-amnestic variants of AD versus clinical variants of FTD. Conclusion: We conclude that PCC hypometabolism is not a discriminant feature to distinguish non-amnestic variants of AD from clinical variants of FTD—and should be interpreted with caution in patients with young-onset, non-amnestic dementia.
Metabolic Changes Detected by 18F-FDG PET in the Preclinical Stage of Familial Creutzfeldt-Jakob Disease
Authors: Lu Hui , Jing Donglai , Chen Yaojing , Cui Chunlei , Gao Ran , Wang Lin , Liang Zhigang , Chen Kewei , Wu Liyong
Published: September 2020

Background: Pathologic processes in Creutzfeldt-Jakob disease (CJD) are not fully understood. Familial CJD (fCJD) gives opportunities to discover pathologic changes in the preclinical stage. Objective: To investigate cerebral glucose metabolism in the preclinical stage via 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) in fCJD. Methods: Seven asymptomatic carriers of G114V mutation and six family members without PRNP mutation from the same fCJD kindred were included, and were followed for 2 years. Ten symptomatic CJD patients were also recruited. All subjects underwent standardized clinical examinations and 18F-FDG PET scans. Results were compared in three groups: baseline carriers against non-carriers (baseline analysis), changes after 2 years in carriers (follow-up analysis), and differences between symptomatic CJD patients and healthy controls (CJD patients analysis). Results: No carriers developed any neurological symptoms during 2-year follow-up. Baseline analysis: carriers demonstrates decreased metabolism (p < 0.001) in left and right postcentral, left fusiform, left superior temporal, left lingual, left superior parietal, and left Heschl gyrus. Follow-up analysis shows metabolic decline (p < 0.001) in right inferior temporal, left supra-marginal and left postcentral lobe, and increased metabolism (p < 0.001) in left fusiform, left angular, left thalamus, left Heschl’s, right Rolandic operculum, and left superior parietal gyrus. CJD patients demonstrates decreased metabolism in right inferior triangularis frontal gyrus, right middle occipital gyrus, right putamen, right thalamus, and right middle temporal gyrus. Conclusion: Hypo-metabolism of parietal and temporal lobe can be detected by 18F-FDG PET in the preclinical stage of CJD. Subcortical area might compensate in the preclinical stage and decompensate in the symptomatic stage.
Epigenetic Regulation of BMAL1 with Sleep Disturbances and Alzheimer’s Disease
Authors: Hulme Bethany , Didikoglu Altug , Bradburn Steven , Robinson Andrew , Canal Maria , Payton Antony , Pendleton Neil , Murgatroyd Chris
Published: September 2020

Background: An early symptom of Alzheimer’s disease (AD) is a disturbance of the circadian rhythm that is associated with disrupted sleep/wake cycles. Objective: To investigate if BMAL1, a key gene that drives the circadian cycle, is epigenetically regulated in brains in relation to longitudinal changes in cognition, sleep quality, and AD neuropathology. Methods: Frontal cortex tissues were acquired from the Manchester Brain Bank (N = 96). DNA methylation at six CpG sites at the promoter of BMAL1, determined using bisulfite pyrosequencing, was tested for associations with Braak stage, CERAD score and Thal phase, longitudinal changes in cognition, sleep measurements and cross-section measures of depressive symptoms (BDI score). Results: Methylation across all the CpGs strongly correlated with each other. We found increased CpG2 methylation with higher Braak (t(92), p = 0.015) and CERAD (t(94), p = 0.044) stages. No significance was found between longitudinal fluid intelligence, processing speed and memory tests, but methylation at CpG1 (r = 0.20, p = 0.05) and CpG4 (r = 0.20, p = 0.05) positively correlated with vocabulary. CpG2 positively correlated with cross-sectional fluid intelligence (r = 0.20 p = 0.05) and vocabulary (r = 0.22 p = 0.03). Though longitudinal analysis revealed no significance between sleep duration, midsleep and efficiency for any of the CpG sites, CpG3 (B = 0.03, 95% CI, p = 0.03) and CpG5 (B = 0.04, 95% CI, p = 0.01) significantly correlated with night wake. CpG4 correlated with depressive symptoms (B = –0.27, 95% CI, p = 0.02). Conclusion: Methylation of BMAL1 associated with tau pathology, changes in cognitive measures, a measure of sleep and depressive symptoms, suggesting an involvement of the circadian cycle.
Neuromodulation for Apathy in Alzheimer’s Disease: A Double-Blind, Randomized, Sham-Controlled Pilot Study
Authors: Padala Prasad R. , Boozer Eugenia M. , Lensing Shelly Y. , Parkes Christopher M. , Hunter Cassandra R. , Dennis Richard A. , Caceda Ricardo , Padala Kalpana P.
Published: September 2020

Background: Apathy, a profound loss of motivation, initiation, and goal directed cognition, is a common comorbidity of Alzheimer’s disease (AD). The presence of apathy is associated with rapid progression of AD, long-term impairment, disability, and higher mortality. Pharmacological treatments of apathy are limited. Objective: The primary objective was to evaluate the efficacy of repetitive transcranial magnetic stimulation (rTMS) for apathy in AD. Methods: A randomized, double-blind, parallel-arm, sham-controlled pilot study was conducted in subjects with AD and apathy (N = 20). Subjects were randomized to rTMS or sham treatment (5 days/week) for four weeks. Primary outcome, apathy evaluation scale-clinician version (AES-C), and secondary outcome measures, modified-Mini Mental State Examination (3MS), instrumental activities of daily living (IADL), and clinical global impression (CGI), were assessed at baseline and four weeks. Follow-up visits were conducted at 8 and 12 weeks to test the durability of effects of intervention. Results: Mean age was 77.3 (±7.2) years, 80% were Caucasians and 10% were females. After adjusting for baseline, there was a significantly greater improvement in the AES-C with rTMS compared to sham treatment (–10.1 (–15.9 to –4.3); t (16)  = –3.69; p = 0.002) at 4 weeks. There was also significantly greater improvement in 3MS (6.9 (1.7 to 12.0); t (15)  = 2.85; p = 0.012), IADL (3.4 (1.0 to 5.9); χ21 = 7.72; p = 0.006), CGI-S (1.4 (0.5 to 2.3), t (16)  = 3.29; p = 0.005), and CGI-I (–2.56 (–3.5 to –1.6), t (17)  = –5.72; p < 0.001) for rTMS compared to the sham at 4 weeks. The effects of rTMS were durable at 12 weeks. Conclusion: rTMS may be safely used in subjects with AD and may improve apathy, function, and some aspects of cognition.
Profile recognition and quantitative evaluation of corrosion in coated conductors based on PMEC with the modified Theta Map
Authors: Yan Bei , Li Yong , Ren Shuting , Hu Jinhua , Chen Hongen , Abidin Ilham Mukriz Zainal
Published: September 2020

Coated conductive structures play an important role in such industrial fields as aerospace, energy, chemical engineering, etc. Because of the corrosive and hostile environments, the corrosion which is normally hidden under the conductor coating, occurs in the surface of the in-service structure. It poses a severe threat to the integrity and safety of the structure. Therefore, it is imperative to effectively detect and quantitatively evaluate the hidden corrosion via Non-destructive Evaluation (NDE) techniques. Pulse-modulation Eddy Current technique (PMEC) has been identified to be superior to other electromagnetic NDE methods, particularly regarding evaluation and imaging of corrosion. Whereas, the acquired corrosion images hardly indicate the true opening profile of the corrosion. This hinders the quantitative evaluation of the corrosion. In light of this, in this paper PMEC along with the image processing technique for imaging and evaluation of corrosion in coated conductors is intensively investigated. The image processing technique is proposed based on Theta Map (TM) which is a processing method for quantifying the source parameters of magnetic data that describe the anomalous structure. The validity of PMEC combined with modified TM for profile recognition and assessment of hidden corrosion in nonmagnetic structures is identified via experiments.
Electromagnetic torque and strength analysis of combination rotor in high speed PMSM
Authors: Lin Hao , Geng Haipeng , Yu Lie , Li Hao , Feng Sheng , Yang Baisong
Published: September 2020

For a high-speed permanent magnet synchronous motor supported by air foil bearings, analyzing the influence of the electromagnetic torque on the strength of the combination rotor was necessary. In this study, the electromagnetic torque was obtained using the analytical calculation and the finite element simulation. The contact interface stress analytical equation affected by the electromagnetic torque was elaborated. A three-dimensional finite element model simulating the combination rotor was constructed, and the stress calculations were performed. The strength of the combination rotor met the design requirement. It properly reflected the characteristics of the electromechanical analysis in the combination rotor. The operation experiments were realized to verify the electromagnetic performance and the strength of the combination rotor. The simulation and experimental results were significant for the electromechanical design and analysis of the combination rotor.
A novel two-input asymmetric dynamic cross-coupled hysteresis modeling and identification for piezoelectric stack actuators
Authors: Dong Yangyang , Zuo Kungang , Han Shaojie , Zhang Zijian
Published: September 2020

Piezoelectric Stack Actuators (PEAs) have been widely used in high-precision positioning system because of its fast response. However, under different excitation voltages and external loads with dynamic frequencies, the width and inclination of PEAs hysteresis curves verified correspondingly, and exhibit asymmetry phenomenon. The main contribution of this paper is to present a two-input asymmetric dynamic cross-coupled hysteresis (TADCH) model to describe the complex nonlinear hysteresis of PEAs in dynamic excitation and loading engineering applications. Moreover, we also develop the corresponding identification method of TADCH model. Finally, several experiments are carried out to verify the accuracy of the proposed model.
Analysis of structure factors affecting suspension force of permanent magnet system with variable magnetic flux path control
Authors: Xu Fangchao , Guo Yongquan , Zhou Ran , Jin Junjie , Zhao Chuan , Zhang Xiaoyou , Sun Feng
Published: September 2020

To solve the problem of reduction of suspension force of permanent magnet system with variable magnetic flux path control, according to the structure of the system, suspension principle of the permanent magnet system with variable magnetic flux path control and the generation principle of the load torque, the influence of the mechanical structure of the system on the suspension force is analyzed by changing part of parameters of the system structure. The results show that the existence of magnetic isolation plate is the main reason for the decrease of suspension force, the permanent magnet ring can be thickened to 11.91 mm, the annular gap can be reduced to 1 mm, thickness of the “F” shaped magnetizer can be increased to 9 mm to increase the suspension force.
Drugging DNA Damage Repair Pathways for Trinucleotide Repeat Expansion Diseases
Authors: Benn Caroline L. , Gibson Karl R. , Reynolds David S.
Published: September 2020

DNA damage repair (DDR) mechanisms have been implicated in a number of neurodegenerative diseases (both genetically determined and sporadic). Consistent with this, recent genome-wide association studies in Huntington’s disease (HD) and other trinucleotide repeat expansion diseases have highlighted genes involved in DDR mechanisms as modifiers for age of onset, rate of progression and somatic instability. At least some clinical genetic modifiers have been shown to have a role in modulating trinucleotide repeat expansion biology and could therefore provide new disease-modifying therapeutic targets. In this review, we focus on key considerations with respect to drug discovery and development using DDR mechanisms as a target for trinucleotide repeat expansion diseases. Six areas are covered with specific reference to DDR and HD: 1) Target identification and validation; 2) Candidate selection including therapeutic modality and delivery; 3) Target drug exposure with particular focus on blood-brain barrier penetration, engagement and expression of pharmacology; 4) Safety; 5) Preclinical models as predictors of therapeutic efficacy; 6) Clinical outcome measures including biomarkers.
Synchronization Management in Hypermedia Documents
Authors: Beladam Naima , Ghomari Abdelghani
Published: September 2020

In this paper, we study the problem of synchronization management in hypermedia documents where we proposed a formal approach inspired by separation logic to analyze and correct progressively the temporal, spatial and hypermedia synchronization constraints. For that purpose, we have developed a tool named Hypermedia Document Builder (HDB) to help the authors to design and verify easily all the hypermedia documents constraints’ errors. So, the latter is presented immediately during the design phase to facilitate their correction and to minimize their number. Our HDB tool permits also to adapt the analyzed hypermedia documents to its different standard web presentation formats such as: HTML5 or SMIL 3.0. Finally, the HDB tool has been compared with tools of similar nature of recent related works and the results show that the proposed tool advances the design of such tools.
An image super-resolution method for better cognition of images in cognition computing system
Authors: Chen Lihui , Zhang Rongzhu , Ahmad Awais , Jeon Gwanggil , Yang Xiaomin
Published: September 2020

Data cognition plays an important role in cognitive computing. Cognition of low-resolution (LR) image is a long-stand problem because LR images have insufficient information about objects. For better cognition of LR images, a multi-resolution residual network (MRRN) is proposed to improve image resolution in this paper for cognitive computing systems. In MRRN, a multi-resolution feature learning (MRFL) strategy is introduced to achieve satisfying performance with low computational costs. Inspired by image pyramids, a feature pyramid is designed to implement multi-resolution feature learning in the building unit of the proposed MRRN. Specifically, multi-resolution residual units (MRRUs) are introduced as the building units of the proposed network, which consist of a feature pyramid decomposition stage and a feature reconstruction stage. To obtain informative features, transferred skip links (TSLs) are utilized to transfer fine-grain residual features in the pyramid decomposition stage to the reconstruction stage. The effectiveness of MRFL and TSL is demonstrated by ablation experiments. Also, the tests on standard benchmarks indicate the superiority of the proposed MRRN over other state-of-the-art methods.
Ensemble Gaussian mixture model-based special voice command cognitive computing intelligent system
Authors: Saravanan P. , Sri Ram E. , Jangiti Saikishor , Ponmani E. , Ravi Logesh , Subramaniyaswamy V.
Published: September 2020

Dysarthria is a speech disorder caused by stroke, Parkinson’s disease, neurological injury, or tumors that damage the nervous system and weaken the speech quality. Developing a unique voice command system for Dysarthric speech helps to recognize impaired speech and convert them into text or input commands. Hidden Markov Model (HMM) is one of the widely used generative model-based classifiers for Dysarthric speech recognition. But due to insufficient training data, HMM doesn’t provide optimal results on overlapping classes. We propose an ensemble Gaussian mixture model to recognize impaired speech more accurately. Our model converts the sequence of feature vectors into a fixed dimensional representation of patterns with varying lengths. The performance efficiency of the proposed model is evaluated on the Dysarthric UA-speech benchmark dataset. The discriminatory information provided by the proposed approach yields better classification accuracy even for shallow intelligibility words compared to conventional HMM.
Development of a reliable supply chain system using blockchain
Authors: Bragadeesh Srinivasan Ananthanarayanan , Umamakeswari Arumugam
Published: September 2020

Traceability and food quality are significant challenges in realizing a reliable food supply chain. The reliability of data in supply chains is one of the critical factors. Ensuring transparency, integrity, and availability is the primary requirement for establishing a proper supply chain network. Blockchain is a distributed structure of immutable records that are chained together to form blocks. It provides a guarantee of storing the data correctly and reliably. Smart contracts, which are self-executing contracts containing the terms of the agreement between the entities involved, provide utility for automation of reputation calculation with the transactions. Reputation systems allow participants to rate each other, thus building trust through reputation. The present reputation systems have bounded scrutiny and lack granularity; hence they are not ideal for supply chains. In this work, we propose a reliable supply chain framework using blockchain and smart contracts. It uses a consortium blockchain network to trace communication between the participants and to calculate reputation scores dynamically. Rewards and penalties are assigned to the participants of the supply chain network based on the food product quality involved in the trade. The network participants have defined roles and the access permissions govern who can access the ledger. An immutable ledger stores all the transactions occurring in the network. Any change in one block will reflect in the consecutive blocks, which ensures the data is reliable and secure. The proposed system is implemented using Hyperledger Composer. The proposed framework is evaluated in terms of throughput and latency for varying asset size and batch size using the benchmarking tool Caliper. Results show that the security and reliability provided by the proposed framework justify the overheads in contrast to a trading model that does not include a blockchain network.
A contribution of fuzzy logic to sustainable tourism through a case analysis in Brazil
Authors: de Paula Luciano Barcellos , Gil-Lafuente Anna María , Alvares Daniela F.
Published: September 2020

Society is increasingly concerned about environmental, social and economic issues. According to the World Tourism Organization, over the past six decades, tourism has experienced a continuous expansion and diversification to become one of the fastest-growing economic sectors in the world. Furthermore, studies affirm the complexity of the tourism sector and the fact that sustainable development depends on various topics that are not correctly identified by managers and policymakers. For these reasons, this paper aims to reflect on the effects of tourism and to propose alternatives that can be sustainably managed. In terms of results, knowledge gaps have been identified and, through a case analysis in Brazil, the forgotten effects of tourism activity that can have an impact on sustainable development have been exposed. Also, an algorithm has been presented to manage uncertainty and facilitate decision-making.
Application of wireless network and machine learning algorithm in entrepreneurship education of remote intelligent classroom
Authors: Lu Heping
Published: September 2020

Educational information system is a hot topic in education today, and informatization is not only reflected in teaching methods. With the development of computer vision and deep learning technologies and the gradual maturity of related hardware, the application of computer algorithms and intelligent identification in distance education has become a norm. This research studies the entrepreneurial model of distance intelligent classrooms, uses machine learning technology as the basis, and combines intelligent image recognition technology to identify the status and expression of students in distance education classrooms. Moreover, this paper has carried out a more detailed study of face detection and expression recognition technology and tried to apply it to classroom teaching evaluation, which has shown certain feasibility in experiments. At the end of this article, the system was tested and analyzed with the collected data, which verified the feasibility and accuracy of the system.
Target recognition of sport athletes based on deep learning and convolutional neural network
Authors: Liu Yuzhong , Ji Yuliang
Published: September 2020

The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.
Simulation analysis of financial stock market based on machine learning and GARCH model
Authors: Tian Jie , Wang Yaoqiang , Cui Wenjing , Zhao Kun
Published: September 2020

With the rapid development of the world’s financial industry, the complexity and relevance of risks are gradually increasing. At present, there are still some deficiencies in the model for measuring financial risk. In view of this, this study analyzes the financial stock market and combines VAR model and GARCH model to conduct financial analysis. Moreover, this study uses the standard deviation in the statistical characteristics of the data to characterize the fluctuation of futures, and then uses the univariate GARCH model to measure the fluctuation. In addition, this study combines the examples to analyze the effectiveness of the model, and compares the predicted data with the actual data to verify the model performance. The results show that the algorithm proposed in this paper has certain effectiveness, and through this research algorithm, investors, speculators or macro decision makers in the futures market can obtain some inspiration.
Automatic translation of spoken English based on improved machine learning algorithm
Authors: Lin Lin , Liu Jie , Zhang Xuebing , Liang Xiufang
Published: September 2020

Due to the complexity of English machine translation technology and its broad application prospects, many experts and scholars have invested more energy to analyze it. In view of the complex and changeable English forms, the large difference between Chinese and English word order, and insufficient Chinese-English parallel corpus resources, this paper uses deep learning to complete the conversion between Chinese and English. The research focus of this paper is how to use language pairs with rich parallel corpus resources to improve the performance of Chinese-English neural machine translation, that is, to use multi-task learning to train neural machine translation models. Moreover, this research proposes a low-resource neural machine translation method based on weight sharing, which uses the weight-sharing method to improve the performance of Chinese-English low-resource neural machine translation. In addition, this study designs a control experiment to analyze the effectiveness of this study model. The research results show that the model proposed in this paper has a certain effect.
Simulation of student classroom behavior recognition based on cluster analysis and random forest algorithm
Authors: Chonggao Pang
Published: September 2020

Classroom student behavior recognition has important guiding significance for the development of distance education strategies. At present, the accuracy of students’ classroom behavior recognition algorithms has problems. In order to improve the effect of distance education student status analysis, this study combines the traditional clustering analysis algorithm and the random forest algorithm to improve the traditional algorithm and combines the human skeleton model to identify students’ classroom behavior in real time. Moreover, this research combines with the needs of students’ classroom behavior recognition to build a network topology model. The error rate of feature reconstruction using spatio-temporal features is lower than that of a single feature. Through experiments, this study verifies the effectiveness of the extracted spatial angle features based on the human skeleton model. The results of algorithm performance test show that the proposed algorithm network structure is superior to the network structure of single feature extraction algorithm.
Image detection and basketball training performance simulation based on improved machine learning
Authors: Pengyu Wang , Wanna Gao
Published: September 2020

Basketball player detection technology is an important subject in the field of computer vision and the basis of related image processing research. This study uses machine learning technology to build a basketball sport feature recognition model. Moreover, this research mainly takes the characteristic information of basketball in the state of basketball goals as the starting point and compares and analyzes the detection methods by detecting the targets in the environment. By comprehensively considering the advantages and disadvantages of various methods, a method suitable for the subject is proposed, namely, a fast skeleton extraction and model segmentation method. The fitting effect of this method, whether in terms of compactness or quantity, has greater advantages than traditional bounding boxes, and realizes the construction of dynamic ellipsoidal bounding boxes in a moving state. In addition, this study designs a controlled trial to verify the analysis of this research model. The research results show that the model proposed in this paper has certain effects and can improve practical guidance for competitions and basketball players training.
Prediction model of college students entrepreneurship ability based on artificial intelligence and fuzzy logic model
Authors: Huang Zhaokun , Liu Guanjun
Published: September 2020

The fundamental solution to the problems of college students’ employment is to encourage college students to start their own businesses. Only by using entrepreneurship to promote employment can the real solution of China’s higher education employment problems be truly solved. Aiming at the current situation of college students’ entrepreneurship and employment, this paper builds a model system suitable for college students’ employment and entrepreneurship forecast and guidance through artificial intelligence algorithms and fuzzy logic models. The diversity-enhanced employment recommendation system developed in this paper uses the MVC three-tier architecture. Moreover, the diversity-enhanced employment recommendation system designed in this paper provides two recommendation methods: individual diversity optimization and overall diversity optimization, which takes into account the relationship between students’ personal interests and employment work. In addition, the system uses the basic idea of user-based collaborative filtering. Finally, this paper designs a control experiment to analyze the performance of this research model. The research shows that the entrepreneurship employment forecast and guidance model constructed in this paper has a certain effect.
Research on peasants poverty alleviation under ecological constraint based on neural network algorithm and network features
Authors: Qu Xiaojuan
Published: September 2020

Aiming at the actual problems encountered in the specific poverty alleviation work, this article designs a management system specifically designed for poverty alleviation workers to solve poverty alleviation data sharing and online editing and uploading of poverty alleviation logs. Based on the neural network and network characteristics, a system model is constructed, and the application of structural disturbance theory in dynamic networks is studied. Moreover, in this study, the dynamic change information between time-series networks is taken into account for structural disturbances. By combining structural disturbances and local topology, a new similarity measurement method suitable for dynamic networks is proposed. In addition, this study proposes an algorithm based on evolutionary clustering and density clustering to detect the structure of dynamic communities. Finally, this study compares the proposed method with the classic method in the artificial network and the real network and analyzes the performance of the research model through data analysis. The research results show that the model constructed in this paper has good performance.
Digitalization system of ancient architecture decoration art based on neural network and image features
Authors: Xin Wu , Daping Qiu
Published: September 2020

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.
Slicing based efficient privacy preservation technique with multiple sensitive attributes for safe data distribution
Authors: Murugaboopathi G. , Gowthami V.
Published: September 2020

Privacy preservation in data publishing is the major topic of research in the field of data security. Data publication in privacy preservation provides methodologies for publishing useful information; simultaneously the privacy of the sensitive data has to be preserved. This work can handle any number of sensitive attributes. The major security breaches are membership, identity and attribute disclosure. In this paper, a novel approach based on slicing that adheres to the principle of k-anonymity and l-diversity is introduced. The proposed work withstands all the privacy threats by the incorporation of k-means and cuckoo-search algorithm. The experimental results with respect to suppression ratio, execution time and information loss are satisfactory, when compared with the existing approaches.
Certain types of coverings based rough sets with application
Authors: Nawar A.S. , El-Bably M.K. , El-Atik Abd El Fattah
Published: September 2020

Covering-based rough sets are important generalizations of the classical rough sets of Pawlak. In this paper, by means of j-neighborhoods, complementary j-neighborhoods and j-adhesions, we build some new different types of j-covering approximations based rough sets and study related properties. Also, we explore the relationships between the considered j-covering approximations and investigate the properties of them. Using different neighborhoods, some different general topologies are generated as topologies induced from a binary relation. Finally, an interesting application of the new types of covering-based rough sets to the rheumatic fever is given.
Incomplete high dimensional data streams clustering
Authors: Najib Fatma M. , Ismail Rasha M. , Badr Nagwa L. , Gharib Tarek F.
Published: September 2020

Many recent applications such as sensor networks generate continuous and time varying data streams that are often gathered from multiple data sources with some incompleteness and high dimensionality. Clustering such incomplete high dimensional streaming data faces four constraints which are 1) data incompleteness, 2) high dimensionality of data, 3) data distribution, 4) data streams’ continuous nature. Thus, in this paper, we propose the Subspace clustering for Incomplete High dimensional Data streams (SIHD) framework that overcomes the above clustering issues. The proposed SIHD provides continuous missing values imputation for incomplete streams based on the corresponding nearest-neighbors’ intervals. An adaptive subspace clustering mechanism is proposed to deal with such incomplete high dimensional data streams. Our experimental results using two different data sets prove the efficiency of the proposed SIHD framework in clustering such incomplete high dimensional data streams in terms of accuracy, precision, sensitivity, specificity, and F-score compared to five algorithms GFCM, GBDC-P2P, DS, Ensemble, and DMSC. The proposed SIHD improved: 1) the accuracy on average over the five algorithms in the same mentioned order by 11.3%, 10.8%, 6.5%, 4.1%, and 3.6%, 2) the precision by 15%, 10.6%, 6.4%, 4%, and 3.5%, 3) the sensitivity by 16.6%, 10.6%, 5.8%, 4.2%, and 3.6%, 4) the specificity by 16.8%, 10.9%, 6.5%, 4%, and 3.5%, 5) the F-score by 16.6%, 10.7%, 6.6%, 4.1%, and 3.6%.