bluuebunny/crossref_metadata_2025 huggingface dataset:
Geodetic observations for constraining mantle processes in Antarctica
Authors: Scheinert Mirko , Engels Olga , Schrama Ernst J. O. , van der Wal Wouter , Horwath Martin
Published: September 2021

Geodynamic processes in Antarctica such as glacial isostatic adjustment (GIA) and post-seismic deformation are measured by geodetic observations such as global navigation satellite systems (GNSS) and satellite gravimetry. GNSS measurements have comprised both continuous measurements and episodic measurements since the mid-1990s. The estimated velocities typically reach an accuracy of 1 mm a
−1
for horizontal velocities and 2 mm a
−1
for vertical velocities. However, the elastic deformation due to present-day ice-load change needs to be considered accordingly.

Space gravimetry derives mass changes from small variations in the inter-satellite distance of a pair of satellites, starting with the GRACE (Gravity Recovery and Climate Experiment) satellite mission in 2002 and continuing with the GRACE-FO (GRACE Follow-On) mission launched in 2018. The spatial resolution of the measurements is low (about 300 km) but the measurement error is homogeneous across Antarctica. The estimated trends contain signals from ice-mass change, and local and global GIA signals. To combine the strengths of the individual datasets, statistical combinations of GNSS, GRACE and satellite altimetry data have been developed. These combinations rely on realistic error estimates and assumptions of snow density. Nevertheless, they capture signals that are missing from geodynamic forward models such as the large uplift in the Amundsen Sea sector caused by a low-viscous response to century-scale ice-mass changes.
A first approach to a Quaternary geomorphological map of the German seas
Authors: Breuer Sonja , Asch Kristine
Published: September 2021

Our paper presents the first draft of a geomorphological map of the German North Sea and Baltic Sea. The inspiration for this map comes from the international collaboration of marine researchers within the European
EMODnet Geology Project
(
https://www.emodnet-geology.eu/
). The current climate change intensifies the natural processes of change in nature. Within the framework of various nature conservation projects, the importance of marine sediment structures on marine fauna and their reproductive cycles, sedimentation conditions, currents, etc. has been investigated. In order to be able to make statements for the German seas and document changes, the current state must first be recorded.
Intelligent Restoration of Historical Parametric Geometric Patterns by Zernike Moments and Neural Networks
Authors: Hajebi Bita , Hajebi Pooya
Published: September 2021

Historical Islamic ornaments include a fantastic treasury of geometric and mathematical algorithms. Inevitably, restoration of these ornaments in periodic patterns consisting of repeated elements has been faced following and substituting the other available similar ingredients instead of vanished parts. Still, the prediction of parametric, quasi, or non-periodic patterns, where components are not identical, needs to be carried out in a more challenging process than the periodic ones due to shape, scale, or angle of rotation alteration. Intelligent restoration could facilitate the forecasting of damaged parts in such geometric patterns that an algorithm has changed their geometric characteristics. In some architectural heritage, geometric patterns include a parametric algorithm like parametric patterns in the ceiling of Sheikh Lotfollahmosque in Isfahan, Iran, and the dominant structure of Persian domes Karbandi. In this article, the aim is to propose a new method for the smart restoration of the parametric geometric patterns in which, by having access to the image of the existing patterns, the vanished parts could be reconstructed spontaneously. Our approach is based on image processing by detecting boundaries of deterioration, finding every individual element, and extracting features of detected individual patterns via Zernike moments. The order of individual patterns starts from the farthest pattern to detected deterioration. Then by creating a time series, the Back-propagation neural network would be trained by extracted features, and the vanished patterns’ features could be predicted and reconstructed. Eventually, the reconstructed and real patterns are compared to determine differences between them by mean-squared error and to evaluate the performance of our method. To validate the process, a parametric geometric pattern is designed by the assumption that some parts are disappeared. The proposed method’s results, in this case, hold an efficient performance with the accuracy of 92.99%. Furthermore, Sheikh Lotfollah’s patterns and Naseredin Mirza mansion’s patterns as two real cases are tested by the proposed method, representing reliable and suitable performance results.
Integrated Power Signature Generation Circuit for IoT Abnormality Detection
Authors: Thompson David , Wang Haibo
Published: September 2021

This work presents a methodology to monitor the power signature of IoT devices for detecting operation abnormality. It does not require bulky measurement equipment thanks to the proposed power signature generation circuit which can be integrated into LDO voltage regulators. The proposed circuit is implemented using a 130 nm CMOS technology and simulated with power trace measured from a wireless sensor. It shows the generated power signature accurately reflects the power consumption and can be used to distinguish different operation conditions, such as wireless transmission levels, data sampling rates and microcontroller UART communications.
Low-overhead Hardware Supervision for Securing an IoT Bluetooth-enabled Device: Monitoring Radio Frequency and Supply Voltage
Authors: Elkanishy Abdelrahman , Furth Paul M. , Rivera Derrick T. , Badawy Abdel-Hameed A.
Published: September 2021

Over the past decade, the number of Internet of Things (IoT) devices increased tremendously. In particular, the Internet of Medical Things (IoMT) and the Industrial Internet of Things (IIoT) expanded dramatically. Resource restrictions on IoT devices and the insufficiency of software security solutions raise the need for smart Hardware-Assisted Security (HAS) solutions. These solutions target one or more of the three C’s of IoT devices: Communication, Control, and Computation. Communication is an essential technology in the development of IoT. Bluetooth is a widely-used wireless communication protocol in small portable devices due to its low energy consumption and high transfer rates. In this work, we propose a supervisory framework to monitor and verify the operation of a Bluetooth system-on-chip (SoC) in real-time. To verify the operation of the Bluetooth SoC, we classify its transmission state in real-time to ensure a secure connection. Our overall classification accuracy is measured as 98.7%. We study both power supply current (IVDD) and RF domains to maximize the classification performance and minimize the overhead of our proposed supervisory system.
LargeGraph
Authors: Zhang Yu , Peng Da , Liao Xiaofei , Jin Hai , Liu Haikun , Gu Lin , He Bingsheng
Published: September 2021

Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system
LargeGraph
. Different from existing out-of-GPU-memory systems, LargeGraph proposes a
dependency-aware data-driven execution approach
, which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and also high parallelism. Specifically, according to the dependencies between the vertices, it only loads and processes the graph data associated with dependency chains originated from active vertices for smaller access cost. Because most active vertices frequently use a small evolving set of paths for their new states’ propagation because of power-law property, this small set of paths are dynamically identified and maintained and efficiently handled on the GPU to accelerate most propagations for faster convergence, whereas the remaining graph data are handled over the CPU. For out-of-GPU-memory graph processing, LargeGraph outperforms four cutting-edge systems: Totem (5.19–11.62×), Graphie (3.02–9.41×), Garaph (2.75–8.36×), and Subway (2.45–4.15×).
The Impact of Incarceration on Recidivism
Authors: Loeffler Charles E. , Nagin Daniel S.
Published: September 2021

The US prison population stands at 1.43 million persons, with an additional 740,000 persons in local jails. Nearly all will eventually return to society. This review examines the available evidence on how the experience of incarceration is likely to impact the probability that formerly incarcerated individuals will reoffend. Our focus is on two types of studies, those based on the random assignments of cases to judges, called judge instrumental-variable studies, and those based on discontinuities in sentence severity in sentencing grids, called regression discontinuity studies. Both types of studies are designed to account for selection bias in nonexperimental estimates of the impact of incarceration on reoffending. Most such studies find that the experience of postconviction imprisonment has little impact on the probability of recidivism. A smaller number of studies do, however, find significant effects, both positive and negative. The negative, recidivism-reducing effects are mostly in settings in which rehabilitative programming is emphasized and the positive, criminogenic effects are found in settings in which such programming is not emphasized. The findings of studies of pretrial incarceration are more consistent—most find a deleterious effect on postrelease reoffending. We also conclude that additional work is needed to better understand the heterogeneous effects of incarceration as well as the mechanisms through which incarceration effects, when observed, are generated. For policy, our conclusion of the generally deleterious effect of pretrial detention adds to a larger body of evidence pointing to the social value of limiting its use.
Mass Transfer at the Ocean–Atmosphere Interface: The Role of Wave Breaking, Droplets, and Bubbles
Authors: Deike Luc
Published: September 2021

Breaking waves modulate the transfer of energy, momentum, and mass between the ocean and atmosphere, controlling processes critical to the climate system, from gas exchange of carbon dioxide and oxygen to the generation of sea spray aerosols that can be transported in the atmosphere and serve as cloud condensation nuclei. The smallest components, i.e., drops and bubbles generated by breaking waves, play an outsize role. This fascinating problem is characterized by a wide range of length scales, from wind forcing the wave field at scales of [Formula: see text](1 km–0.1 m) to the dynamics of wave breaking at [Formula: see text](10–0.1 m); air bubble entrainment, dynamics, and dissolution in the water column at [Formula: see text](1 m–10 μm); and bubbles bursting at [Formula: see text](10 mm–1 μm), generating sea spray droplets at [Formula: see text](0.5 mm–0.5 μm) that are ejected into atmospheric turbulent boundary layers. I discuss recent progress to bridge these length scales, identifying the controlling processes and proposing a path toward mechanistic parameterizations of air–sea mass exchange that naturally accounts for sea state effects.
Prospects for the Global Elimination of Hepatitis B
Authors: Block Timothy M. , Chang Kyong-Mi , Guo Ju-Tao
Published: September 2021

Chronic hepatitis B virus (HBV) infection is the leading cause of liver cirrhosis and hepatocellular carcinoma, estimated to be globally responsible for ∼800,000 deaths annually. Although effective vaccines are available to prevent new HBV infection, treatment of existing chronic hepatitis B (CHB) is limited, as the current standard-of-care antiviral drugs can only suppress viral replication without achieving cure. In 2016, the World Health Organization called for the elimination of viral hepatitis as a global public health threat by 2030. The United States and other nations are working to meet this ambitious goal by developing strategies to cure CHB, as well as prevent HBV transmission. This review considers recent research progress in understanding HBV pathobiology and development of therapeutics for the cure of CHB, which is necessary for elimination of hepatitis B by 2030.
Viruses and Metabolism: The Effects of Viral Infections and Viral Insulins on Host Metabolism
Authors: Girdhar Khyati , Powis Amaya , Raisingani Amol , Chrudinová Martina , Huang Ruixu , Tran Tu , Sevgi Kaan , Dogus Dogru Yusuf , Altindis Emrah
Published: September 2021

Over the past decades, there have been tremendous efforts to understand the cross-talk between viruses and host metabolism. Several studies have elucidated the mechanisms through which viral infections manipulate metabolic pathways including glucose, fatty acid, protein, and nucleotide metabolism. These pathways are evolutionarily conserved across the tree of life and extremely important for the host's nutrient utilization and energy production. In this review, we focus on host glucose, glutamine, and fatty acid metabolism and highlight the pathways manipulated by the different classes of viruses to increase their replication. We also explore a new system of viral hormones in which viruses mimic host hormones to manipulate the host endocrine system. We discuss viral insulin/IGF-1-like peptides and their potential effects on host metabolism. Together, these pathogenesis mechanisms targeting cellular signaling pathways create a multidimensional network of interactions between host and viral proteins. Defining and better understanding these mechanisms will help us to develop new therapeutic tools to prevent and treat viral infections.
Activation of the Ae4 (Slc4a9) cation-driven Cl−/HCO3− exchanger by the cAMP-dependent protein kinase in salivary gland acinar cells
Authors: Peña-Münzenmayer Gaspar , Kondo Yusuke , Salinas Constanza , Sarmiento José , Brauchi Sebastián , Catalán Marcelo A.
Published: September 2021

We found that Ae4 exchanger activity in secretory salivary gland acinar cells is increased upon β-adrenergic receptor stimulation. The activation of Ae4 was prevented by H89, a nonselective PKA inhibitor. Protein sequence analysis revealed two residues (S173 and S273) that are potential targets of cAMP-dependent protein kinase (PKA). Experiments in CHO-K1 cells expressing S173A and S273A mutants showed that S173A, but not S273A, is not activated by PKA.
Heterogeneity of human adaptations to bed rest and hypoxia: a retrospective analysis within the skeletal muscle oxidative function
Authors: Salvadego Desy , Grassi Bruno , Keramidas Michail E. , Eiken Ola , McDonnell Adam C. , Mekjavic Igor B.
Published: September 2021

This retrospective study was designed to analyze the interindividual variability in the responses of different variables characterizing the skeletal muscle oxidative function to normoxic (N-BR) and hypoxic (H-BR) bed rests and to a hypoxic ambulatory confinement (H-AMB) of 10 and 21 days. We also assessed whether and how the addition of hypoxia to bed rest might influence the heterogeneity of the responses. In vivo measurements of O2 uptake and muscle fractional O2 extraction were carried out during an incremental one-leg knee-extension exercise. Mitochondrial respiration was assessed in permeabilized muscle fibers. A total of 17 subjects were included in this analysis. This analysis revealed a similar variability among subjects in the alterations induced by N-BR and H-BR both in peak O2 uptake (SD: 4.1% and 3.3% after 10 days; 4.5% and 8.1% after 21 days, respectively) and peak muscle fractional O2 extraction (SD: 5.9% and 7.3% after 10 days; 6.5% and 7.3% after 21 days), independently from the duration of the exposure. The individual changes measured in these variables were significantly related ( r = 0.66, P = 0.004 after N-BR; r = 0.61, P = 0.009 after H-BR). Mitochondrial respiration showed a large variability of response after both N-BR (SD: 25.0% and 15.7% after 10 and 21 days) and H-BR (SD: 13.0% and 19.8% after 10 and 21 days); no correlation was found between N-BR and H-BR changes. When added to bed rest, hypoxia altered the individual adaptations within the mitochondria but not those intrinsic to the muscle oxidative function in vivo, both after the short- and medium-term exposures.
A Novel Incentive Mechanism with Government Subsidy for the Key Technologies R&D of New Energy Automobile Industry
Authors: He Ru , Zheng Chui-Yong , Zhao Huan-Huan
Published: September 2021

The key technology research and development (R&D) of new energy automobile industry is a collaborative innovation activity of multi-innovation subjects, and how to set up effective incentive mechanisms and safeguards to stimulate innovative subjects and urge them to invest in corresponding efforts is crucial to ensure the success of key technologies R&D in the new energy automobile industry. To solve the incentive problem of key technology R&D of new energy automobile industry, considering government subsidy and the risk attitude of government and innovation subject, from the perspective of stakeholder negotiation and system coordination, introduce the minimum-cost consensus method into grey target decision-making and set constraint conditions to ensure a stable and effective incentive method. Then use a case to verify the effectiveness and rationality of the proposed model.
Dynamic Analysis of a Model for Spruce Budworm Populations with Delay
Authors: Muhammadhaji Ahmadjan , Halik Azhar
Published: September 2021

A class of delayed spruce budworm population model is considered. Compared with previous studies, both autonomous and nonautonomous delayed spruce budworm population models are considered. By using the inequality techniques, continuation theorem, and the construction of suitable Lyapunov functional, we establish a set of easily verifiable sufficient conditions on the permanence, existence, and global attractivity of positive periodic solutions for the considered system. Finally, an example and its numerical simulation are given to illustrate our main results.
Ivabradine Ameliorates Cardiac Function in Heart Failure with Preserved and Reduced Ejection Fraction via Upregulation of miR‐133a
Authors: Shao Shuai , Zhang Yue , Gong Mengqi , Yang Qian , Yuan Meng , Yuan Ming , Suo Ya , Wang Xinghua , Li Ying , Bao Qiankun , Li Guangping
Published: September 2021

Heart failure (HF) is a clinical syndrome caused by impairment of ventricular filling, ejection of blood, or both and is categorized as HF with reduced ejection fraction (HFrEF) or HF with preserved ejection fraction (HFpEF) based on left ventricular function. Cardiac fibrosis contributes to left ventricular dysfunction and leads to the development of HF. Ivabradine, an If current selective specific inhibitor, has been shown to improve the prognosis of patients with HF. However, the effects of ivabradine on cardiac function and fibrosis in HFpEF and HFrEF and the underlying mechanism remain unclear. In the present study, we utilized mouse models to mimic HFpEF and HFrEF and evaluated the therapeutic effects of ivabradine. By treating mice with different doses (10 mg/kg/d and 20 mg/kg/d) of ivabradine for 4 or 8 weeks, we found that a high dose of ivabradine improved cardiac diastolic function in HFpEF mice and ameliorated cardiac diastolic and systolic function and ventricular tachycardia incidence in HFrEF mice. Moreover, ivabradine significantly reduced the activation of cardiac fibroblasts and myocardial fibrosis in mice. Mechanistically, microRNA‐133a, which was upregulated by ivabradine, targeted connective tissue growth factor and collagen 1 in cardiac fibroblasts and might contribute to the protective role of ivabradine. Together, our work utilized mouse models to study HFpEF and HFrEF, demonstrated the protective role of ivabradine in HFpEF and HFrEF, and elucidated the potential underlying mechanism, which provides an effective strategy for related diseases.
An Association Rule Analysis of the Acupressure Effect on Sleep Quality
Authors: Lin Chih-Hung , Lin Ya-Hsuan , Tzeng I-Shiang , Kuo Chan-Yen
Published: September 2021

Background. Sleep is recognized as an all-important physiological process, which also contributes to maintaining several bodily functions and systems. According to the Pittsburgh Sleep Quality Index (PSQI), also known as the most widely used tool in the field of subjective assessment of self-perceived sleep quality, a combination of acupoints could be more effective than single acupoint treatment in improving sleep quality. Methods. The present study was based on the extracted eligible studies rooted in a previous meta-analysis that worked on the basis of association rule mining and examined the potential kernel acupoint combinations for improving sleep quality. Results. Depending on the Apriori algorithm, we summarized 26 acupoints as binary data from the 32 eligible studies based on a previous meta-analysis and analyzed them. The top 10 most frequently selected acupoints were HT7, SP6, PC6, KI1, GV20, EM5, EX-HN3, EX-HN16, KI3, and MA-TF1. Furthermore, as deduced from 21 association rules, the primary relevant rules in the combination of acupoints are (EX-HN3, EX-HN16)=>(GV20) and (HT7, KI1)=>(PC6). Conclusions. In order to use acupuncture to improve sleep quality, integrating (EX-HN3, EX-HN16, GV20) with (HT7, KI1, PC6) acupoints could be deemed as the kernel acupoint combination.
Effect of Micronutrients on Thyroid Parameters
Authors: Krishnamurthy Hari Krishnan , Reddy Swarnkumar , Jayaraman Vasanth , Krishna Karthik , Song Qi , Rajasekaran Karenah E. , Wang Tianhao , Bei Kang , Rajasekaran John J.
Published: September 2021

Micronutrients are involved in various vital cellular metabolic processes including thyroid hormone metabolism. This study aimed to investigate the correlation between serum levels of micronutrients and their effects on thyroid parameters. The correlation of serum levels of micronutrients and thyroid markers was studied in a group of 387 healthy individuals tested for thyroid markers (T4, T3, FT4, FT3, TSH, anti-TPO, RT3, and anti-Tg) and their micronutrient profile at Vibrant America Clinical Laboratory. The subjects were rationalized into three groups (deficient, normal, or excess levels of micronutrients), and the levels of their thyroid markers were compared. According to our results, deficiency of vitamin B2, B12, B9 and Vit-D25[OH] (

p
<
0.05

) significantly affected thyroid functioning. Other elemental micronutrients such as calcium, copper, choline, iron, and zinc (

p
<
0.05

) have a significant correlation with serum levels of free T3. Amino acids asparagine (r = 0.1765,

p
<
0.001

) and serine (r = 0.1186,

p
<
0.05

) were found to have a strong positive correlation with TSH. Valine, leucine, and arginine (

p
<
0.05

) also exhibited a significant positive correlation with serum levels of T4 and FT4. No other significant correlations were observed with other micronutrients. Our study suggests strong evidence for the association of the levels of micronutrients with thyroid markers with a special note on the effect of serum levels of certain amino acids.
Tree Index Nearest Neighbor Search of Moving Objects along a Road Network
Authors: Jiang Wei , Wei Fangliang , Li Guanyu , Bai Mei , Ren Yongqiang , An Jingmin
Published: September 2021

With the widespread application of location‐based service (LBS) technology in the urban Internet of Things, urban transportation has become a research hotspot. One key issue of urban transportation is the nearest neighbor search of moving objects along a road network. The fast‐updating operations of moving objects along a road network suppress the query response time of urban services. Thus, a tree‐indexed searching method is proposed to quickly find the answers to user‐defined queries on frequently updating road networks. First, a novel index structure, called the double tree‐hash index, is designed to reorganize the corresponding relationships of moving objects and road networks. Second, an index‐enhanced search algorithm is proposed to quickly find the k‐nearest neighbors of moving objects along the road network. Finally, an experiment shows that compared with state‐of‐the‐art algorithms, our algorithm shows a significant improvement in search efficiency on frequently updating road networks.
Nursing Research on Patients with Chronic Obstructive Pulmonary Disease and Respiratory Failure Based on Big Data
Authors: Chen Yongqing , Pan Luyi
Published: September 2021

This work organically integrates a systematic and individualized nursing plan with big data technology and applies it to the care of patients with chronic obstructive pulmonary disease (COPD) and respiratory failure (RF) and explores the continuous care model based on modern big data technologies to improve COPD and RF. It aims to relieve the symptoms of COPD and RF, reduce the number of acute episodes of COPD and RF and the number of hospitalizations, and improve the quality of life of patients. One hundred COPD and RF patients hospitalized in the respiratory medicine department of a tertiary hospital were selected and were categorized into control and experimental group. The nursing mode of the patients in the control group was the original telephone follow-up in the department, and the contents of the follow-up were determined according to the questions of the patients on the telephone at that time. Based on the original nursing in the department, the experimental group adopted individualized continual nursing plans based on the Internet and big data techniques for patients to conduct a pulmonary rehabilitation-related functional assessment, functional exercise guidance, and health guidance. Experimental results show that, compared with traditional continuous care, individualized continuous care combined with big data techniques can improve the lung function of patients with stable COPD and RF, reduce the number of acute COPD and RF attacks and the number of readmissions, and improve self-management ability and quality of life. The method can be applied and implemented in continuous nursing care.
Self-Recurrent Learning and Gap Sample Feature Synthesis-Based Object Detection Method
Authors: Jiang Lvjiyuan , Wang Haifeng , Kai Yan  , Zhou Chengjiang , Li Songlin , Dang Junpeng , Chang Rong , Peng Jie , Fang Yanbin , Dai Chenkai , Yang Yang
Published: September 2021

Object detection-based deep learning by using the looking and thinking twice mechanism plays an important role in electrical construction work. Nevertheless, the use of this mechanism in object detection produces some problems, such as calculation pressure caused by multilayer convolution and redundant features that confuse the network. In this paper, we propose a self-recurrent learning and gap sample feature fusion-based object detection method to solve the aforementioned problems. The network consists of three modules: self-recurrent learning-based feature fusion (SLFF), residual enhancement architecture-based multichannel (REAML), and gap sample-based features fusion (GSFF). SLFF detects objects in the background through an iterative convolutional network. REAML, which serves as an information filtering module, is used to reduce the interference of redundant features in the background. GSFF adds feature augmentation to the network. Simultaneously, our model can effectively improve the operation and production efficiency of electric power companies’ personnel and guarantee the safety of lives and properties.
Mechanical Properties of Composite Materials from Waste Poly(ethylene terephthalate) Reinforced with Glass Fibers and Waste Window Glass
Authors: Worku Biruk Gedif , Wubieneh Tessera Alemneh
Published: September 2021

After primary uses of the plastic product, most developing countries like Ethiopia are facing a shortage of postconsumer disposal waste sites and it became a very serious problem on environmental pollution due to its nonbiodegradable nature. For this reason, regenerating and using the waste product as resources and reducing environmental pollutions are a great opportunity. This research is aimed at the manufacturing of composite materials from waste poly(ethylene terephthalate) (PET) bottles reinforced with glass fibers and filled with waste glass powder for floor tile applications. The tile composites were prepared by the melt-mixing method followed by compression molding. The effect of filler, fiber, and PET matrix loading on the composite was investigated using their tensile, compression, and flexural strength tests. The sample was characterized using a universal testing machine. PerkinElmer FTIR instrument was also used. For this, eleven samples prepared by varying the glass fiber weight % from 0 to 10, PET matrix weight % from 70 to 85, and glass powder filler weight % from 5 to 20. The measurement results of the composite were maximum tensile strength (81.625 MPa) and flexural strength (1067.59 MPa) recorded at 10%weight of glass fiber, 85% weight of PET matrix, and 5%weight of window glass filler. The maximum compressive strength is 1876.14 MPa at 10% weight glass fiber, 70 wt% PET matrix, and 20 wt% window glass filler. Based on this, the tensile strength and flexural strength increased with increased weight % of glass fiber and decreased with increased window glass filler. The FTIR spectrum shows some of the groups that have been removed from the recycled PET; this explains the brittleness of the recycled PET as compared to the waste bottle PET. The microstructure was uniformly distributed, and the material became opaque, probably because the decrease in chain length improves chain packing, increasing the crystallinity degree and crystal size.
Application of the Artificial Neural Network in the Diagnosis of Infantile Bronchial Bridge with 64-Slice Multislice Spiral CT
Authors: Li Gengwu , Wang Chang , Lin Huihui , Li Xu , Hu Jun
Published: September 2021

The objective is to study the application of spiral CT in the diagnosis of the trachea in children. In this study, the effect of 64-slice multislice spiral CT in diagnosing infant bronchial bridge was studied based on an artificial neural network. From June 2020 to December 2020, 100 cases of children with the trachea in the outpatient department of our hospital were selected as the research object. They were divided into the study group and the control group, with 50 cases in each group. The results showed that among the 50 cases in the control group, 42 cases were found to have a bronchial foreign body and 8 cases were missed; the detection rate was 84%. There were 7 cases of trachea foreign body, 15 cases of left bronchial foreign body, 14 cases of right bronchial foreign body, and 6 cases of medium bronchial foreign body. The detection rate of the study group was significantly higher than that of the control group, with a statistical significance (

P
<
0.05

). Conclusion. The detection rate of neural networks in MSCT is higher than that of X-ray, and the MSCT based on the artificial neural network can clearly show the morphology, position, and the relationship between the foreign body and trachea, which is worthy of clinical promotion and application.
Perceptual Feedback Mechanism Sensor Technology in e‐Commerce IoT Application Research
Authors: Guo Yixuan , Liang Gaoyang
Published: September 2021

With the development of sensor technology and the Internet of Things (IoT) technology, the trend of miniaturization of sensors has prompted the inclusion of more sensors in IoT, and the perceptual feedback mechanism among these sensors has become particularly important, thus promoting the development of multiple sensor data fusion technologies. This paper deeply analyzes and summarizes the characteristics of sensory data and the new problems faced by the processing of sensory data under the new trend of IoT, deeply studies the acquisition, storage, and query of sensory data from the sensors of IoT in e‐commerce, and proposes a ubiquitous storage method for massive sensory data by combining the sensory feedback mechanism of sensors, which makes full use of the storage resources of IoT storage network elements and maximally meets the massive. In this paper, we propose a ubiquitous storage method for massive sensing data, which makes full use of the storage resources of IoT storage network elements to maximize the storage requirements of massive sensing data and achieve load‐balanced data storage. In this paper, starting from the overall development of IoT in recent years, the weak link of intelligent information processing is reinforced based on the sensory feedback mechanism of sensor technology.
Risk Identification and Assessment during the Excavation of the Deep Foundation Pit
Authors: Meng Guowang , Li Xiang , Liu Hao , Wu Bo , Liu Yi , Huang Wei , Wang Yong , Xu Shixiang
Published: September 2021

This study presents a new model for identifying and evaluating high‐risk factors in foundation pit excavation. The model combines the fuzzy decision‐making trial and the evaluation laboratory (FDEMATEL), the entropy weight method, and the multiattributive border approximation area comparison (MABAC) method. Firstly, the risk factors such as geology, surrounding environment, monitoring, construction, and management are studied in detail. Secondly, the subjective weight is calculated by the fuzzy DEMATEL method, and the objective weight is calculated by the entropy weight method. Then, the MABAC method is introduced to identify the key risk factors of the foundation pit and the risk level of foundation pit construction. Finally, Jinan Rail Transit R2 Line Kaiyuan Road Station is used as a case study for analysis based on the risk assessment model. The results show that the model can identify key risk factors in different construction stages of foundation pits, which can provide guidance for risk management decision‐making.
Family Cohesion Is Associated with the Self‐Perceived Need for Dental Treatment among Adolescents
Authors: Prata Isolda M. L. F. , Granville-Garcia Ana Flávia , Neves Érick T. B. , Lima Larissa C. M. , Dutra Laio C. , Perazzo Matheus F. , Ferreira Fernanda M. , Paiva Saul M.
Published: September 2021

This study explored the association between family cohesion and self‐perceived need for dental treatment among adolescents. A school‐based representative cross‐sectional study was conducted with 746 students aged 15 to 19 randomly selected from schools in Campina Grande, Brazil. Parents/guardians provided information on sociodemographic data, and students completed questionnaires about the self‐perceived need for dental treatment, dental pain, and family cohesion and adaptability (FACES III). Two dentists were trained (kappa >0.80) to diagnosis dental caries using the Nyvad criteria and assess adolescents’ level of functional oral health literacy (BREALD‐30). Descriptive analysis was performed, followed by nonadjusted and adjusted robust binary logistic regression for complex samples (α = 5%). The prevalence of self‐perceived need for dental treatment was 88.6%. The presence of dental caries (OR = 2.10; IC 95%: 1.22–3.61), tooth loss (OR = 15.81; IC 95%: 2.14–116.56), dental pain in the last six months (OR = 1.87; IC 95%: 1.06–3.31), and enmeshed family cohesion type (OR = 10.23; IC 95%: 3.96–26.4) remained associated with the self‐perceived need for dental treatment in the final model. In conclusion, dental caries, dental pain, tooth loss, and family cohesion influenced the self‐perceived need for dental treatment in adolescents.
Can Robots Get Some Human Rights? A Cross-Disciplinary Discussion
Authors: Persaud Priya , Varde Aparna S. , Wang Weitian
Published: September 2021

An autonomous household robot passed a self-awareness test in 2015, proving that the cognitive capabilities of robots are heading towards those of humans. While this is a milestone in AI, it raises questions about legal implications. If robots are progressively developing cognition, it is important to discuss whether they are entitled to justice pursuant to conventional notions of human rights. This paper offers a comprehensive discussion of this complex question through cross-disciplinary scholarly sources from computer science, ethics, and law. The computer science perspective dissects hardware and software of robots to unveil whether human behavior can be efficiently replicated. The ethics perspective utilizes insights from robot ethics scholars to help decide whether robots can act morally enough to be endowed with human rights. The legal perspective provides an in-depth discussion of human rights with an emphasis on eligibility. The article concludes with recommendations including open research issues.
[Retracted] Comprehensive Evaluation of Teaching Ability Based on Network Communication Environment
Authors: Su Yawen , Chen Guofu , Li Moyan
Published: September 2021

With the continuous development of school teaching quality, the measurement of the current teaching ability level has become a concern of people. This research mainly discusses the comprehensive evaluation of the teaching ability level based on the network communication environment. This study uses fuzzy comprehensive evaluation method to comprehensively evaluate the system. First, obtain the representative evaluation standards of education informatization and a series of indicators related to informatization teaching at home and abroad; secondly, conduct a comprehensive analysis of the various evaluation standards and related indicators of informatization teaching that have been obtained; finally, the process in each indicator is discussed and revised by experts and relevant information technology teachers, and a number of schools are selected to conduct small‐scale experimental evaluation experiments to form information‐based teaching evaluation indicators. In this study, the proportion method is used to calculate the membership degree of each observation index based on the recovered data and establish the fuzzy relationship matrix of each evaluation factor. Paying attention to the level of index weights can guide the development direction of informatization teaching and promote the healthy development of informatization teaching. Therefore, this article uses the expert scoring method combined with the analytic hierarchy process to determine the weights in the evaluation index system. There is a significant difference between teachers in urban areas and teachers in rural areas (p < 0.05). The ratio of each grade of the teacher’s informatization teaching ability comment collection is “bad 0.5%,” “relatively poor 5.7%,” “normal 35.3%,” “good 53.1%,” and “excellent 5.3%.” This research will help promote the improvement of school teaching quality.
Effect of Nitrogen-Doped Carbon Dots (NCDs) on the Characteristics of NCD/MIL-53(Fe) Composite and Its Photocatalytic Performance for Methylene Blue Degradation under Visible Light
Authors: Nguyen Ngoc Tue , Nguyen Xuan Truong , Nguyen Duc-Trung , Tran Hong Minh , Nguyen Thi May , Tran Thuong Quang
Published: September 2021

Metal-organic framework composites, which are combined from metal-organic framework and advanced carbon material, have drawn great attention in many fields of application such as environmental remediation and catalysts. Within this paper, the carbon/MIL-53(Fe) composite was fabricated via an in situ synthesis, in which N-containing carbon dots (NCDs) were mixed with MOF precursors’ solutions under various ratios before going through the solvothermal stage. It was showed that the introduction of a certain amount of NCDs would affect characteristic features and improve the photocatalytic performance of final products. The optimal doping content of NCDs in NCD/MIL-53(Fe) composite was determined. SEM images showed that the M-140 appeared as hexagonal bipyramid-shaped crystals with an average size of 700 nm. Compared with pristine MIL-53(Fe), the M-140 was more visibly light-responsive, and its calculated band gap energy was approximately 2.3 eV. In addition, M-140 catalyst also displayed more excellent photocatalytic activity for Methylene Blue degradation in a pH range from 5 to 7. Under optimal conditions, MB was achieved within 60 minutes and the removal rate was nearly 100% after 5 cycles. The photocatalytic mechanism of the obtained NCD/MIL-53(Fe) composite was discussed.
Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network
Authors: Zhang Dengqing , Chen Yunyi , Chen Yuxuan , Ye Shengyi , Cai Wenyu , Jiang Junxue , Xu Yechuan , Zheng Gongfeng , Chen Ming
Published: September 2021

In recent decades, heart disease threatens people’s health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.
Health Index Monitoring of Sports Injury Rehabilitation Training Based on Wearable Sensors
Authors: Yi Jiang , Zou Yuepei
Published: September 2021

In order to design the perception system of the lower limb wearable rehabilitation robot, this study established the kinematics theoretical model of human lower limb and conducted the kinematics analysis of human body. By using the dynamic attitude analysis system, combined with the human body mark points, the position data of human body mark points in the process of standing up, sitting up, walking, stepping up, and squatting were collected. Combined with the movement mechanism of human lower limbs, the characteristics of human motion state transition are analyzed, and the perceptual algorithm for judging human motion intention is studied, so as to determine the wearer’s current posture, standing intention while sitting, walking intention while standing, moving intention, and stopping intention during walking. The results show that the angle of the hip joint changes regularly between 0° and 37° and the angle of the knee joint changes regularly between 0° and 70°during the standing process, which is consistent with the angle change trajectory collected by the dynamic attitude analysis system. The angle trajectories of the hip and knee joints measured by the absolute angle sensor are the same as those obtained by the dynamic attitude analysis system. 1.5 rad and 0.3 rad were selected as reasonable and effective thresholds for determining sitting and standing states.
The Therapeutic Effect of Traditional LiuJunZi Decoction on Ovalbumin-Induced Asthma in Balb/C Mice
Authors: Xu Wenting , Zhao Rui , Yuan Bin
Published: September 2021

Aim. To investigate the therapeutic effect of LiuJunZi decoction (LJZD) in an experimental model of asthma and uncover its potential mechanism. Materials and Methods. The ovalbumin (OVA) was applied to induce asthma in Balb/C mice, and LJZD was orally administrated to asthmatic mice. The lung function and histological lesion were evaluated by airway hyperresponsiveness assay, lung edema assay, and hematoxylin and eosin staining. The amounts of CD4+CD25+Foxp3+ TReg cells were analyzed through combining fluorescent antibody staining with flow cytometry assay. The levels of inflammatory factors and immunoglobulins were detected by enzyme-linked immuno sorbent assay (ELISA). The expression of miR-21 and miR-146a was investigated by real-time PCR. The protein expression of activating protein-1 (AP-1), nuclear factor kappa-B (NF-κB), and NF-κB inhibitor alpha (IκBα) was determined by western blotting. Results. LJZD improves OVA-induced asthma in Balb/C mice, which is manifested by decreasing lung edema, Penh levels, lung histological lesion, and inflammatory cell infiltration. LJZD increased the number of CD4+CD25+Foxp3+ TReg cells in blood mononuclear cells from asthmatic mice. Furthermore, LJZD reduced the levels of tumor necrosis factor-α (TNF-α), interleukin- (IL-) 4, IL-6, IgG1, and IgE, but increased interferon gamma (IFN-γ) expression, in serum of asthmatic mice, and also decreased the expression of IL-17a, IL-23, IL-25, and thymic stromal lymphopoietin (Tslp) in lung tissues. In addition, miR-21 and miR-146a expression and phospho (p)-NF-κB, p-IκBα, and AP-1 protein expression were inhibited by LJZD in lung tissues from asthmatic mice. Conclusion. LJZD improved OVA-induced asthma in Balb/C mice by inhibiting allergic inflammation and Th2 immunoreaction, which might be associated with the inactivation of the NF-κB signaling pathway.
circCOL1A1 Promotes the Progression of Gastric Cancer Cells through Sponging miR-145 to Enhance RABL3 Expression
Authors: Ma Yue , Ren Yanyi , Wen Huitao , Cui Chengcheng
Published: September 2021

Circular RNA has been reported to be a new noncoding RNA which plays important roles in tumor progression. One of the most common functions of circular RNA is to regulate microRNA expression by acting as a microRNA sponge. However, the circular RNA expression profile and function remain mostly unclear in gastric cancer. In the study, we explored the expression and function of circCOL1A1 (hsa_circ_0044556) in gastric cancer. We performed RT-PCR with divergent primers, mRNA stability assay, and RNase R digestion assay to characterize circCOL1A1 in gastric cancer cell lines. qRT-PCR was applied to detect the level of circCOL1A1 in both gastric cancer cell lines and tissues. Gain- and loss-of-function studies were carried out to detect the influence of circCOL1A1 on gastric cancer cells by performing CCK8, migration, and invasion assays. The regulation of the downstream genes was identified by qRT-PCR, western blot assay, dual luciferase assay, and RNA pull-down assay. The results showed that circCOL1A1 was highly expressed in both gastric cancer cells and tissues. Silence of circCOL1A1 inhibited the proliferation, migration, and invasion of gastric cancer cells. circCOL1A1 regulated the expression of miR-145 by acting as a microRNA sponge, and the influence of circCOL1A1 could be abrogated by miR-145 mimics. Our research shows that miR-145 plays its functions through targeting and regulating RABL3. Inhibition of circCOL1A1/miR-145/RABL3 could effectively suppress gastric cancer cell proliferation, migration, and invasion. circCOL1A1 also promote the transformation of M1 into M2 macrophage. Our study identified circCOL1A1 as a novel oncogenic circRNA and will provide more information for gastric cancer therapy.