bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Study on the Impact of endf/b-vii.1 Nuclear Library Uncertainty on the CERCER and CERMET Fueled Accelerator-Driven System Calculation Results Using Monte Carlo Method Codes
Authors: Vu Thanh Mai , Hartanto Donny
Published: March 2022

A new design of a small power accelerator-driven system (ADS) using inert matrix fuel—ceramic–ceramic matrix (CERCER) and ceramic-metallic matrix (CERMET) was proposed in our previous work for high transmutation rate, small reactivity loss due to burnup, and enhanced safety features. The fast neutron spectrum in the ADS showed a benefit for reducing minor actinides inventories in the fuel cycle. However, the reliability of the nuclear cross section data of evaluated libraries, especially at high energy, needs to be investigated and its impact on CERCER and CERMET fueled ADS calculation results needs to be assessed. In this study, the impact of endf/b-vii.1 nuclear library uncertainty on the CERCER and CERMET fueled ADS reactivity calculation are evaluated using the Monte Carlo method (scale6.2 and whisper-1.1). The sensitivity coefficients and uncertainty obtained by the two codes show a good agreement. The results reveal the uncertainty of the endf/b-vii.1 nuclear data caused significant uncertainty in the reactivity calculations (about 1000 pcm) and it is particularly higher in the CERMET fueled core due to a harder neutron spectrum. The paper identifies the major contributes—isotopes and related cross sections—to the core reactivity uncertainty by suggesting the enhancement of these nuclear data.
Finite Element Analysis and Bench Testing of Ventricular Septal Defect Occluder
Authors: Li Yiming , Song Chengli , Sun Kun
Published: March 2022

The mechanical behavior of the ventricular septal defect (VSD) occluder is strongly linked to complications following transcatheter closure of the VSD. Methods: A mechanical model of the VSD occluder was constructed by theoretical modeling of the braiding structure. The mechanical properties of the VSD occluders with different braiding angles (30 deg, 45 deg, 60 deg), materials (nitinol (NiTi), polydioxanone (PDO)), and waist-heights (3 mm, 4 mm) were studied by finite element analysis and validated by bench tests. Under 1 mm radial shrinkage, the bending angles at the waist of the 30 deg NiTi, 45 deg NiTi, 60 deg NiTi and 45 deg PDO occluders were 112 deg, 121 deg, 155 deg and 155 deg, respectively. The maximum principal strains at the waist were 16.62%, 8.19%, 1.20%, and 0.66%, respectively. With 0.5 rad axial bending, the maximum radial deformations at the waist were 1.73, 1.44, 0.41 and 1.68 mm, respectively. When the occluders were implanted into VSD with the mean thickness of 3.5 mm, considerable stress developed at the hole's margin and the contact area. The area with the 3 mm occluder was much bigger than that with the 4 mm occluder. Conclusions: the 60 deg NiTi occluder showed better ability to fit the deformation of the defect than the other NiTi occluders, and the 45 deg PDO occluder performed better under compression conditions but poorly under bending conditions than the 45 deg NiTi occluder. The choice of the appropriate waist-height is beneficial to eliminate associative complication by reducing the contact stress.
Legacy Branding: The Posthumous Utilization and Management of Athlete Brands
Authors: Williams Antonio S. , Pedersen Zack P. , Brummett Kelly J.
Published: March 2022

The passing of basketball icon Kobe Bryant at the beginning of 2020 was devastating for many different sporting and cultural communities. However, the plethora of opportunities Bryant left his family, and the management of those entities by his estate, thereafter, shed light on a neglected area of branding research. How athletes are able to prepare their estates to continue to benefit from their name, image, and likeness, even after death, is a substantial topic in regard to the legacy that various athletes are able to establish. Through an analysis of various posthumous branding phenomena, as well as a comparison with other posthumous celebrity brands, this commentary discusses the current issues faced by athletes, such as ownership and protection. An understanding of current barriers to greater posthumous earnings will benefit how athletes and researchers alike construct and evaluate brands, respectively. Future research should address how prevalent forward thinking is to athletes’ brand building toward a successful postathletic career, as well as the current status of estate planning and brand communication by athletes and/or their brand managers.
“Food First but Not Always Food Only”: Recommendations for Using Dietary Supplements in Sport
Authors: Close Graeme L. , Kasper Andreas M. , Walsh Neil P. , Maughan Ronald J.
Published: March 2022

The term “food first” has been widely accepted as the preferred strategy within sport nutrition, although there is no agreed definition of this and often limited consideration of the implications. We propose that food first should mean “where practically possible, nutrient provision should come from whole foods and drinks rather than from isolated food components or dietary supplements.” There are many reasons to commend a food first strategy, including the risk of supplement contamination resulting in anti-doping violations. However, a few supplements can enhance health and/or performance, and therefore a food only approach could be inappropriate. We propose six reasons why a food only approach may not always be optimal for athletes: (a) some nutrients are difficult to obtain in sufficient quantities in the diet, or may require excessive energy intake and/or consumption of other nutrients; (b) some nutrients are abundant only in foods athletes do not eat/like; (c) the nutrient content of some foods with established ergogenic benefits is highly variable; (d) concentrated doses of some nutrients are required to correct deficiencies and/or promote immune tolerance; (e) some foods may be difficult to consume immediately before, during or immediately after exercise; and (f) tested supplements could help where there are concerns about food hygiene or contamination. In these situations, it is acceptable for the athlete to consider sports supplements providing that a comprehensive risk minimization strategy is implemented. As a consequence, it is important to stress that the correct terminology should be “food first but not always food only.”
The Compositional Impacts of 2 Distinct 24-Hour Movement Behavior Change Patterns on Physical Fitness in Chinese Adolescents
Authors: Zhang Ting , Li Hongjuan , Li Chao , Zhang Liu , Zhang Zhaohua
Published: March 2022

Background: The study aimed to examine predicted differences of 2 different behavior change patterns on physical fitness (PF). Methods: Participants were 241 students (51% girls) aged 11–14 years from China. Light physical activity, moderate to vigorous physical activity (MVPA), and sedentary behavior (SB) were objectively measured. Sleep was obtained by subtracting from awake time. According to Chinese National student PF standards, 5 components of PF, including body mass index, cardiorespiratory fitness, speed, muscular explosive power and strength endurance, and flexibility, were assessed. The effects of different time reallocations between 24-hour movement behaviors on PF were estimated based on adjusted compositional multiple linear regression models with isometric log ratios. Results: Compared with MVPA substituting for the remaining behaviors, MVPA replacing SB or light physical activity produced more favorable changes on the comprehensive PF score, cardiorespiratory fitness, explosive power, and speed. MVPA replacing 30 minutes of SB was associated with favorable changes in PF (+1.9 [0.53, 3.18] points), 50-m run (−0.17 [−0.31, −0.04] s), long-distance running (−5.54 s [for girls]/7.25 s [for boys]), and long jump (+0.05 [0.01, 0.09] m). When sleep replaced SB, PF improved. Conclusions: MVPA substituting SB or light physical activity is a strategy with a greater improvement in PF.
Brain Activity During Experimental Knee Pain and Its Relationship With Kinesiophobia in Patients With Patellofemoral Pain: A Preliminary Functional Magnetic Resonance Imaging Investigation
Authors: Barber Foss Kim D. , Slutsky-Ganesh Alexis B. , Diekfuss Jed A. , Grooms Dustin R. , Simon Janet E. , Schneider Daniel K. , Jayanthi Neeru , Lamplot Joseph D. , Hill Destin , Pombo Mathew , Wong Philip , Reiter David A. , Myer Gregory D.
Published: March 2022

Context: The etiology of patellofemoral pain has remained elusive, potentially due to an incomplete understanding of how pain, motor control, and kinesiophobia disrupt central nervous system functioning. Objective: To directly evaluate brain activity during experimental knee pain and its relationship to kinesiophobia in patients with patellofemoral pain. Design: Cross-sectional. Methods: Young females clinically diagnosed with patellofemoral pain (n = 14; 14.4 [3.3] y; body mass index = 22.4 [3.8]; height = 1.61 [0.1] m; body mass = 58.4 [12.7] kg). A modified Clarke test (experimental pain condition with noxious induction via patella pressure and quadriceps contraction) was administered to the nondominant knee (to minimize limb dominance confounds) of patients during brain functional magnetic resonance imaging (fMRI) acquisition. Patients also completed a quadriceps contraction without application of external pressure (control contraction). Kinesiophobia was measured using the Tampa Scale of Kinesiophobia. The fMRI analyses assessed brain activation during the modified Clarke test and control contraction and assessed relationships between task-induced brain activity and kinesiophobia. Standard processing for neuroimaging and appropriate cluster-wise statistical thresholds to determine significance were applied to the fMRI data (z > 3.1, P < .05). Results: The fMRI revealed widespread neural activation in the frontal, parietal, and occipital lobes, and cerebellum during the modified Clarke test (all zs > 4.4, all Ps < .04), whereas neural activation was localized primarily to frontal and cerebellar regions during the control contraction test (all zs > 4.4, all Ps < .01). Greater kinesiophobia was positively associated with greater activity in the cerebello-frontal network for the modified Clarke test (all zs > 5.0, all Ps < .01), but no relationships between kinesiophobia and brain activity were observed for the control contraction test (all zs < 3.1, all Ps > .05). Conclusions: Our novel experimental knee pain condition was associated with alterations in central nociceptive processing. These findings may provide novel complementary pathways for targeted restoration of patient function.
The Single Hop for Distance Test: Reviewing the Methodology to Measure Maximum and Repeated Performance
Authors: Judd Alice , Sharp Tim
Published: March 2022

Context: The single hop for distance (SHD) and Limb Symmetry Index are often used to assess readiness for returning to sport postinjury. However, the sensitivity of SHD Limb Symmetry Index for detecting functional abnormalities is questionable due to the methodological inconsistencies in the literature. The common methods use few repetitions; lacking consideration of repeated maximum performance, or the effect of fatigue (or failed attempts) on repeated maximum performance, required for sport. The objectives were to review the SHD for evaluating repeated maximum performance, and the effect of failed attempts on consistency of performance, in a healthy population of sportspersons. Design: A quantitative, nonexperimental, same-subject, repeated-measures design with 15 healthy Cardiff University students. Method: Participants performed the SHD up to 30 times on each limb, with standardized rests. A failed attempt, if the landing was not controlled, was noted but distance not recorded. Participants stopped if 3 consecutive distances decreased, or 30 repetitions were achieved. Results: Average hop distance increased with each repetition. Only the 28th to 30th hops were between the maximum average value and 1 SD below. With failed repetitions included, the maximum average hop was at repetition 21 with more results falling between the maximum and 1 SD below, demonstrating more consistency in repeated performance. The number of failed repetitions had a trend to increase over time; whether subjects overreach in attempt to achieve maximum performance, or whether due to fatigue is unknown. Conclusion: Maximum performance of SHD is not achieved until multiple repetitions have been conducted. Repeated maximum performance does not drop-off until over 21 repetitions have been conducted. Consistency of repeated maximum performance varies depending on whether failed attempts are included in the analysis. Further investigation into the reasons for failed attempts is required to improve clinical decision making for return to sport postinjury.
Optimality, Stability, and Agility of Human Movement: New Optimality Criterion and Trade-Offs
Authors: Latash Mark L.
Published: March 2022

This review of movement stability, optimality, and agility is based on the theory of motor control with changes in spatial referent coordinates for the effectors, the principle of abundance, and the uncontrolled manifold hypothesis. A new optimality principle is suggested based on the concept of optimal sharing corresponding to a vector in the space of elemental variables locally orthogonal to the uncontrolled manifold. Motion along this direction is associated with minimal components along the relatively unstable directions within the uncontrolled manifold leading to a minimal motor equivalent motion. For well-practiced actions, this task-specific criterion is followed in spaces of referent coordinates. Consequences of the suggested framework include trade-offs among stability, optimality, and agility, unintentional changes in performance, hand dominance, finger specialization, individual traits in performance, and movement disorders in neurological patients.
Physical Literacy of Marginalized Middle School Adolescents in Kansas City Public Schools
Authors: Eighmy Katlyn E. , Lightner Joseph S. , Grimes Amanda R. , Miller Teesha
Published: March 2022

Purpose: Physical inactivity among adolescents in the United States continues to be a pervasive and growing problem, especially among low income and adolescents of color. Physical literacy is important for adolescents to engage in physical activity. However, few studies have assessed physical literacy among marginalized populations. The purpose of this study is to describe levels of physical literacy among marginalized adolescents in a Midwest City. Methods: Data were collected from 169 participants (85 adolescents and 84 parents). Adolescents included in the study were from 4 Kansas City (Missouri) public schools. Univariate statistics were calculated for 3 physical literacy domains (PLAYinventory, PLAYself, and PLAYparent). To assess for differences among groups, the authors conducted a single-factor analysis of variance (1-way analysis of variance). Results: The sample (N = 169) was primarily Hispanic (48.2% adolescents and 42.9% parents). Sedentary behaviors were self-reported as the highest overall activities adolescents participated in within the past 12 months. The mean physical literacy score for this sample was 71.9 among adolescent reported and 72.7 among parent reported. Analysis of variance of racial and ethnic groups for PLAYself and PLAYparent assessments showed no significant difference in values. Compared with other subscales of both the PLAYparent and PLAYself instrument, parents and adolescents showed a lack of confidence in adolescent’s ability to be active in the 4 environments (land, water, ice, and snow). Conclusion: Physical literacy is shown to be important in maintaining physical activity throughout life; given this, it is important to understand how to increase confidence of seasonal specific skills in marginalized adolescents.
Development of a Probe System for Measuring the Plasma Parameters and the High-Energy Part of the Electron-Energy Distribution Function
Authors: Saifutdinov A. I. , Sysoev S. S.
Published: March 2022

Abstract—

A probe system has been developed on the basis of an external ADC/DAC module (ADC is the analog-to-digital converter and DAC is the digital-to-analog converter). Using this system, it is possible to determine all the main plasma parameters of continuous and pulsed gas discharges. A program for the Windows operating system has been developed in C++ to control the probe system. The probe system can be used for diagnostics of plasma devices and can be included in modern microplasma analyzers of gas mixtures.
Phase Relations in Spinel Lherzolite KLB-1 According to Results of Thermodynamic Modeling up to 30 GPa: Peculiarities of Mineral Assemblages and Geodynamic Effects
Authors: Perchuk A. L. , Serdyuk A. A.
Published: March 2022

The composition of spinel lherzolite xenolith KLB-1 from the Kilbourne Hole volcanic crater, United States, which is close to the composition of the Earth’s primitive mantle, was used for thermodynamic modeling of phase relations in the Na2O–CaO–FeO–MgO–Al2O3–SiO2 system (NCFMAS) using the Perple_X software package in the temperature range of 900–2000 °C and pressures of 0.0001–30 GPa. The calculated phase diagram is in good agreement with published thermodynamic data on KLB-1 composition and reveals the peculiarities of mantle mineral assemblages at P–T parameters on which experimental data are insufficient or absent. The results showed that the mineral assemblage of garnet wehrlite (garnet + olivine + clinopyroxene), the least common type of mantle peridotite on the Earth’s surface, prevails in the upper mantle since the Archean. Mineral assemblage of garnet lherzolite (garnet + olivine + clinopyroxene + orthopyroxene), which is a variety of mantle peridotites most widely found on the Earth’s surface, is formed in the lithospheric mantle because its temperatures are lower than those of the convecting mantle. Thermodynamic modeling reveals a ringwoodite-free field in the P–T diagram (located at the bottom of the mantle transition zone), which is crosscut by Archean adiabats and by the geotherms of Archean and the hottest Phanerozoic plumes. This area causes a change, from negative to positive, in the slope of the boundary between the lower mantle and the mantle transition zone. A positive slope of the boundary in the Archean should have stimulated the ascend of lower mantle plumes through the transition zone. Conversely, this boundary has a negative slope for most Phanerozoic plumes, rising from the lower mantle, and as a result, the plumes either slow down or stop.
Effectiveness of tax policy changes in Montenegro: smoking behaviour by socio-economic status
Authors: Cizmovic Mirjana , Mugosa Ana , Kovacevic Milica , Lakovic Tanja
Published: March 2022

Background

The main goal of this study was to examine the responsiveness of smoking prevalence and cigarette consumption to price and income changes by income groups and the effectiveness of tax policy changes to reduce cigarette consumption in Montenegro.

Data and methods

A two-part model was applied to estimate smoking participation, smoking intensity price and income elasticity. The first part of the model applies logit regression, while the second uses Deaton’s model to improve the validity and objectivity of conditional (smoking intensity) elasticity results. A generalised linear model (GLM) was applied to verify robustness. The reason for this is that Deaton’s model is commonly used in the analysis of Household Budget Survey (HBS) data, especially when households do not report the market price. Moreover, using this model, it is possible to capture the shading of quality to price change. The analysis used HBS data (2006–2017).

Results

The estimates indicate that tobacco pricing policies had a much higher impact on smoking prevalence in the low-income group (price elasticity of −0.595) relative to the high-income group (price elasticity of −0.344). The same conclusion could be drawn for the smoking intensity elasticity: the high-income group was the least affected by changes in price (price elasticity of −0.258). At the same time, the most affected was the low-income group, with price elasticity of −0.424. Poorer households spent a larger share of their budget on cigarettes. The simulation results confirm that increases in the specific excise taxes of 58.3% on tobacco would reduce total cigarette consumption by 11.25% while increasing the collection of government revenue by 8.07%.

Conclusion

Smoking prevalence and consumption are very responsive to price and income changes, with considerable differences in elasticities between income groups. The taxation policy has a positive impact on changing patterns of consumption and public revenues across each income group. Low-income and middle-income households would benefit the most, while on the other hand, the highest revenue collection was generated from the wealthiest group. Our results align with results obtained so far for other low-income and middle-income countries. This paper contributes to the analysis of the smoking prevalence and cigarette consumption responsiveness to price and income changes, which was conducted for the first time in Montenegro.
Surface influence on the stationary shear deformation of a magnetorheological fluid
Authors: Borin Dmitry , Spörl Eva Marita , Zubarev Andrey , Odenbach Stefan
Published: March 2022

The study experimentally examines the quasi-static shear deformation of a magnetorheological (MR) fluid structured in an external magnetic field. Experiments are carried out using a rheometer with a plate–plate configuration. The working surfaces of the measuring geometry are modified to demonstrate their influence on the response of the field-structured MR fluid. The simplest possible suspension of microparticles of carbonyl iron in mineral oil without using surfactants or any modifiers is used. The difference in results obtained for structured MR fluid with different concentrations of magnetic particles using different modifications of the surface is demonstrated. The results are intended to motivate more intensive research on the issue and further more in-depth theoretical analysis of static elastic properties of structured MR fluids. Certain related critical issues are briefly highlighted.
Spatio-Temporal Context Based Adaptive Camcorder Recording Watermarking
Authors: Hui Chen , Liu Shaohui , Shi Wuzhen , Jiang Feng , Zhao Debin
Published: March 2022

Video watermarking technology has attracted increasing attention in the past few years, and a great deal of traditional and deep learning-based methods have been proposed. However, these existing methods usually suffer from the following two challenges: First, most algorithms cannot resist camcorder recording attack, which limits their practical application. Second, watermark embedding may cause substantial degradation of video quality. Through analyzing the unique distortions presented in the camcorder recording process, including geometric distortion, temporal sampling distortion, sensor distortion and processing distortion, this paper proposes a novel spatio-temporal context based adaptive camcorder recording watermarking scheme STACR. In STACR, considering the geometric distortion and video visual quality, we embed the watermark by constructing a spatio-temporal histogram and incorporate a content features based adaptive locating algorithm to select embedding blocks and embedding strengths. As for the temporal sampling attack, we put forward a watermark correlation-based synchronization algorithm and combine it with cross-validation. Moreover, to resist the sensor distortion, we design a local matching-based algorithm to improve the extraction accuracy. In addition, grouped and repeated embedding strategies are combined to cope with the processing distortion. Experimental results compared with the state-of-the-art show that the proposed scheme achieves high video quality and is robust to geometric attacks, compression, scaling, transcoding, recoding, frame rate changes and especially for camcorder recording.
AABLSTM: A Novel Multi-task Based CNN-RNN Deep Model for Fashion Analysis
Authors: Zhang Xianlin , Shen Mengling , Li Xueming , Wang Xiaojie
Published: March 2022

With the rapid growth of online commerce and fashion-related applications, visual clothing analysis and recognition has become a hotspot in computer vision. In this paper, we propose a novel AABLSTM network, which is based on deep CNN-RNN, to solve the visual fashion analysis of clothing category classification, attribute detection, and landmark localization. The designed fashion model is leveraged with the multi-task driven mechanism as follows: firstly, a
bidirectional LSTM (Bi-LSTM)
branch is proposed for efficiently mining the semantic association between related attributes so as to improve the precision of clothing category classification and attribute detection; then, an imitated hourglass sub-network of “down-up sampling” is constructed for boosting the accuracy of fashion landmark localization; and finally, a specially designed multi-loss function is constructed to better optimize the network training. Extensive experimental results on large-scale fashion datasets demonstrate the superior performance of our approach.
GLPose: Global-Local Representation Learning for Human Pose Estimation
Authors: Jiao Yingying , Chen Haipeng , Feng Runyang , Chen Haoming , Wu Sifan , Yin Yifang , Liu Zhenguang
Published: March 2022

Multi-frame human pose estimation is at the core of many computer vision tasks. Although state-of-the-art approaches have demonstrated remarkable results for human pose estimation on static images, their performances inevitably come short when being applied to videos. A central issue lies in the visual degeneration of video frames induced by rapid motion and pose occlusion in dynamic environments. This problem, by nature, is insurmountable for a single frame. Therefore, incorporating complementary visual cues from other video frames becomes an intuitive paradigm. Current state-of-the-art methods usually leverage information from adjacent frames, which unfortunately place excessive focus on only the temporally nearby frames. In this paper, we argue that combining global semantically similar information and local temporal visual context will deliver more comprehensive and more robust representations for human pose estimation. Towards this end, we present an effective framework, namely
global-local enhanced pose estimation
(
GLPose
) network. Our framework consists of a feature processing module that conditionally incorporates global semantic information and local visual context to generate a robust human representation and a feature enhancement module that excavates complementary information from this aggregated representation to enhance keyframe features for precise estimation. We empirically find that the proposed GLpose outperforms existing methods by a large margin and achieves new state-of-the-art results on large benchmark datasets.
Boosting Hyperspectral Image Classification with Dual Hierarchical Learning
Authors: Wang Shuo , Ben Huixia , Hao Yanbin , He Xiangnan , Wang Meng
Published: March 2022

Hyperspectral image (HSI) classification aims at predicting the pixel-wise labels in an image, where there are only a few labeled pixel samples (hard labels) for training. It is a challenging task since the classification process is susceptible to over-fitting under training with limited samples. To relieve this problem, we propose a method based on dual hierarchical learning. First, we employ a connectionist hyperspectral convolution (HC) network to capture the representations of the pixels from different receptive fields. Specifically, an HC is designed to learn the correlation among adjacent pixels and is further extended to a connectionist hierarchical structure. These operations use the correlation to enhance one-pixel learning from multiple receptive fields. Second, we analyze the properties in the hyperspectral image and introduce a hierarchical pseudo label generation algorithm to enrich the supervision of the label information. Finally, we design a dual hierarchical learning strategy to help all HC layers learn from both the hard labels and the hierarchical pseudo labels. In other words, it addresses the HSI classification problem from different views. For inference, we employ two fusion strategies to find a better prediction. The experimental results on four popular HSI benchmarks, i.e., Salinas-A, IndianPines, PaviaU, and PaviaC, demonstrate the effectiveness of the proposed method. Our code is publicly available on GitHub: https://github.com/ShuoWangCS/HSI-DHL.
An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation
Authors: Djenouri Youcef , Belhadi Asma , Srivastava Gautam , Lin Jerry Chun-Wei
Published: March 2022

This article proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets, which are tweets with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of 90% when analyzing complex Wikipedia data while the other algorithms did not achieve 85% when processing the same amount of data.
Research on the Application of Adaptive Matching Tracking Algorithm Fused with Neural Network in the Development of E-Government
Authors: Lv Peian
Published: March 2022

With the development of information technology and the deepening of government system reform, e-government has become one of the key points of government construction. However, both in developing countries and developed countries, there is a common phenomenon that the success rate of e-government projects is not high. Although various e-government evaluation index systems have been proposed in both theoretical research and application aspects, which provide strong support for the development of e-government, these index systems still have limitations. This paper comprehensively applies BP neural network and adaptive matching tracking, and constructs an e-government performance evaluation model based on adaptive matching neural network. We conduct simulation experiments on the constructed model to verify the accuracy and rationality of the model evaluation. It is feasible to introduce the Balanced Scorecard to construct the E-government performance evaluation index system. The constructed index system has strong objectivity, operability, comprehensiveness, guidance and sustainability, and can be applied to the practice of e-government performance evaluation. It is feasible to use the adaptive matching tracking neural network model to evaluate the performance of e-government. The model has the ability to self-learn the evaluation samples, and can grasp the mapping relationship between the evaluation indicators and the evaluation results, so as to imitate the evaluation of experts, and overcome the evaluation randomness and subjective uncertainty that are difficult to get rid of in traditional manual evaluation. In order to better evaluate the operation status of the e-government system and its adaptability to future needs, and find out its existing problems, this paper fully considers the interaction process between the government and enterprises, and establishes a corresponding responsive government. The application of adaptive matching tracking neural network model for e-government performance evaluation has better optimization results than the original BP neural network model. The specific performance is that the convergence speed is fast, the training will not fall into a local minimum, and the error of the evaluation results is small.
Changes of Entropy Index and Cerebral Oxygen Metabolism in the Maintenance of Remifentanil Anesthesia and Their Predictive Value for Postoperative Hyperalgesia
Authors: Zhang Tianhao , Ma Fang
Published: March 2022

Objective. To explore the changes of entropy index and cerebral oxygen metabolism in the maintenance of remifentanil anesthesia and the predictive value of postoperative hyperalgesia. Methods. A total of 266 patients undergoing general anesthesia in our hospital from January 2020 to October 2021 were selected, and remifentanil was used to maintain anesthesia. The state entropy, reaction entropy, and cerebral oxygen metabolism indexes (cerebral oxygen uptake rate (CERO2), arterial-venous blood oxygen difference (Da-jvO2)) of patients before induction of anesthesia, 15 minutes during the operation, and at the end of the operation were compared. The influencing factors of postoperative hyperalgesia were analyzed. The logistic regression model of postoperative hyperalgesia was established, and the value of entropy index and cerebral oxygen metabolism in predicting postoperative hyperalgesia was evaluated by drawing the receiver operating characteristic curve (ROC). Results. The state entropy, response entropy, and CERO2 at 30 min during the operation and at the end of the operation were lower than those before the induction of anesthesia, and Da-jvO2 was higher than that before the induction of anesthesia (

P
<
0.001

). At the end of the operation, the state entropy, reaction entropy, and CERO2 were higher than 30 minutes during the operation, and Da-jvO2 was lower than 30 minutes during the operation (

P
<
0.001

). The dosage of remifentanil, reaction entropy, and CERO2 at the end of the operation entered the logistic model. The AUC value of the reaction entropy and CERO2 combined to predict postoperative hyperalgesia at the end of the operation was 0.851 greater than the reaction entropy at the end of the operation (

χ

2

=
3.847

,

P
=
0.036

), CERO2 (

χ

2

=
2.589

,

P
=
0.010

) single index predictive value. Conclusion. The entropy index and cerebral oxygen metabolism in general anesthesia patients change with the progress and discontinuation of remifentanil maintenance anesthesia, and the combination of the two has a high predictive power in postoperative hyperalgesia risk assessment. When the

reaction
 
entropy
>
54.23

,

CER

O

2

>
34.14
%

, or the total dosage of

remifentanil
≥
30
 
μ
g
/
kg

at the end of the operation, we should be highly vigilant of the occurrence of postoperative hyperalgesia and postoperative analgesia management should be strengthened.
Evaluation of M&A Risk Based on Adaptive Immune Genetic Algorithm
Authors: Li Huadong , Zhang Shuo
Published: March 2022

In order to improve the convergence and accuracy of M&A risk assessment method, an M&A risk assessment method based on adaptive immune genetic algorithm is proposed. The risk of M&A is divided into M&A preparation stage, M&A implementation stage, and M&A integration stage. The event tree analysis method is used to clarify the inducement of M&A risk events. We optimize the traditional genetic algorithm, design adaptive immune genetic algorithm to ensure the convergence of risk assessment algorithm, select the risk assessment indicators of enterprise M&A, determine the weight of the assessment indicators, and construct the quantitative model of enterprise M&A risk assessment. The experimental results show that the proposed risk assessment method takes less time, has higher accuracy, and has less probability of enterprise data imbalance.
The Detection and Verification of Two Heterogeneous Subgroups and a Risk Model Based on Ferroptosis-Related Genes in Hepatocellular Carcinoma
Authors: Li Jiang , Tao Haisu , Wang Wenqiang , Li Jian , Zhang Erlei
Published: March 2022

#Background. Because of the heterogeneity of hepatocellular carcinoma (HCC) and the complex nature of the tumor microenvironment (TME), the long-term efficacy of therapy continues to be a clinical challenge. It is necessary to classify and refine the appropriate treatment intervention decision-making in this kind of tumor. Methods. We used “ConsensusClusterPlus” to establish a stable molecular classification based on the ferroptosis-related genes (FRGs) expression obtained from FerrDb. The clinical features, immune infiltration, DNA damage, and genomic changes of different subclasses were evaluated. The least absolute shrinkage and selection operator regression (LASSO) method and univariate Cox regression were utilized to construct the ferroptosis-related prognosis risk score (FPRS) model, and the association between the FPRS model and HCC molecular characteristics, immune features, and immunotherapy was studied. Results. We identified two ferroptosis subclasses, C1 with poor prognosis and a higher proportion of patients in the middle and late stages infected with HBV and HCV, having higher DNA damage including aneuploidy, HRD, fraction altered, and the number of segments, and higher probability of gene mutation and copy number mutation. FPRS model was constructed on the basis of differentially expressed genes (DEGs) between C1 and C2, which showed a higher area under the curve (AUC) in predicting overall survival rate in the training set and independent verification cohort and could reflect the clinical characteristics and response to immunotherapy of different patients, being an independent prognostic factor of HCC. Conclusion. Here, we revealed two novel molecular subgroups based on FRGs and develop an FPRS model consisting of six genes that can help predict prognosis and select patients suitable for immunotherapy.
Internet of Things- (IoT-) Based Real-Time Vital Physiological Parameter Monitoring System for Remote Asthma Patients
Authors: Islam Khairul , Alam Farabi , Zahid Abid Ibna , Khan Mohammad Monirujjaman , InamAbbasi Muhammad
Published: March 2022

In this paper, the design and implementation of an asthma patient monitoring system are presented. It can be categorized as an IoT-based device which can monitor heart rate, room temperature, humidity, air quality, nostril temperature, and oxygen saturation (SpO2) using an ESP8266 microcontroller and different sensors. All data will be sent to Firebase via Wi-Fi and then to patients and doctors via mobile applications and websites, respectively. Doctors can access this data via a website and instruct patients based on their specific needs. An ESP8266 microcontroller, a DHT11 temperature and humidity sensor, a MAX30100 pulse and SpO2 sensor, a MQ-135 air quality sensor, and an LM-35 temperature sensor have been used for the proposed system in this research. This system uses an efficient way to store all the data and information about the patient. It also carries their various test reports. The data is preserved in a proper way, which will help the patient or doctor retrieve any information in a smooth and swift manner. The software part of the system is divided into two parts. One of them is a mobile application that was designed by the Android Studio. The second one is the website, and it is also divided into two parts. HTML (hypertext markup language), CSS (cascading style sheets), and JavaScripts are designed for the front end of the website, and PHP (hypertext preprocessor) is used for the backend of the website. The device is tested on seven different real human test subjects, and the accuracy of that device is state of the art. It can measure the heart rate of a person along with their oxygen saturation with accuracy. Also, this device calculates the humidity properly as it changes the moment wet towels are put near the sensors and far from the sensors. It also shows the proper air quality as it has been tested with smoke to measure the extreme difference in the air quality. This system also has video calling and prescription features. Using this system, patients can consult with the doctor and get a prescription. Using this system, the vital physiological parameters of the remote patients can be measured and monitored. Any hospital or diagnostic center can be more efficient and organized by using a system. And patients are also freed from the hustle as they do not need to go and visit the doctor over and over again. The system is designed in such a way that it can look after the particular needs of a typical hospital or a diagnostic center and is able to provide easy and efficient storage of information related to patients.
The Legal Regulation of Artificial Intelligence and Edge Computing Automation Decision-Making Risk in Wireless Network Communication
Authors: Sun Junlei
Published: March 2022

This article is aimed at studying the legal regulation of artificial intelligence and edge computing automated decision-making risks in wireless network communications. The data under artificial intelligence is full of flexibility and vitality, which has changed the way of data existence in the whole society. Its core is various algorithm programs, which determine the existence of artificial intelligence. In this environment, society develops rapidly with unstoppable momentum. However, from a legal perspective, artificial intelligence has algorithmic discrimination, such as gender discrimination, clothing discrimination, and racial discrimination. It does not possess openness, objectivity, and accountability. The consequences are sometimes serious enough to endanger the public interest of the entire society, leading to market disorder, etc. Therefore, the problem of artificial intelligence algorithm discrimination remains to be solved. This article uses algorithms to adjust algorithm discrimination to reduce the harm caused by artificial intelligence algorithm discrimination to a certain extent. First of all, this article introduces a regulatory-based edge cloud computing architecture model. It is mentioned that distributed cloud computing can use subsystems to calculate various resources and storage resources and can make automated decisions when calculating certain data. In order to reduce the impact of algorithm discrimination and trigger data diversification to reduce the probability of discrimination, an edge computing network data capture system is designed. And this article mentions the BP neural network model. The BP neural network model is divided into input layer, output layer, and hidden layer. The training samples are passed from the input layer to the output layer through the hidden layer. If the output information does not meet expectations, the error will be back-propagated, and the connection weight will be adjusted continuously. This paper proposes a deep learning system model in real-time artificial intelligence driven by edge computing. When this model is applied to legal regulations, it can cooperate with edge computing and artificial intelligence algorithms to provide high-precision automated decision-making. Finally, this paper designs an artificial intelligence-assisted automated decision-making experiment based on the theory of legal computing. This paper proposes a Bayesian algorithm that uses edge algorithms to merge into artificial intelligence and verifies the feasibility of this hypothesis through experiments. The experimental results show that it has a certain ability to regulate algorithmic discrimination caused by artificial intelligence in legal regulations. It can improve the regulatory effects of laws and regulations to a certain extent, and the improved artificial intelligence Bayesian algorithm clustering effect of edge computing is increased by about 7.2%.
Effective Oriental Magic for Analgesia: Acupuncture
Authors: Zhang Menglong , Shi Lei , Deng Shizhe , Sang Bomo , Chen Junjie , Zhuo Bifang , Qin Chenyang , Lyu Yuanhao , Liu Chaoda , Zhang Jianli , Meng Zhihong
Published: March 2022

Pain is a kind of complex physiological and psychological symptom, which makes the person debilitated and uncomfortable. Some persistent pain is unbearable for the patients, reducing the quality of life and bringing considerable pressure to the individuals and society. Pain killers seem to be effective in analgesia for patients, but their safety and addiction are crucial issues. From the theory of traditional Chinese medicine (TCM), the blocked meridian is the main cause of pain, and effective acupuncture can play a positive analgesic effect. Acupuncture that can date back thousands of years is one of the ancient medical practices in China. Its safety and effectiveness are respected. Based on its superior safety and inferior side effects, it has been gradually recognized as a therapeutic intervention method for complementary medicine, which is also generally used to treat multiple pain diseases. It is shown by modern medical studies that neurotransmitters are the material basis for the acupuncture effect, and the effect of acupuncture analgesia is related to changes in neurotransmitters. However, the specific mechanism has not been elucidated. This review aims to comprehensively discuss the historical evolution of acupuncture analgesia, clinical research of acupuncture analgesia, comparison of acupuncture and drug therapy, the neurotransmitter mechanism of acupuncture analgesia, the effect of acupuncture manipulation on analgesia, and bibliometric analysis of acupuncture treatment for pain, to explore the superiority and related mechanism of acupuncture analgesia from different aspects, and to provide a more effective treatment for alleviating patients’ pain.
Histogram Shifting-Based Quick Response Steganography Method for Secure Communication
Authors: Peter Geno , Sherine Anli , Teekaraman Yuvaraja , Kuppusamy Ramya , Radhakrishnan Arun
Published: March 2022

Steganography is a tool which allows the data for transmission by concealing secret information in a tremendously growing network. In this paper, a novel technique quick response method (QRM) is proposed for the purpose of encryption and decryption. Existing system uses side match vector quantization (SMVQ) technique which has some challenges such as security issues and performance issues. To handle the security and performance issues, the proposed system uses two methods, namely, quick response method and shifting method. In the proposed system, encoding part calculates the performance for capacity, PSNR (peak signal-to-noise ratio), MSE (mean square error), and SSIM (structural similarity index method), and the decoding part calculates the performance of MSE (mean square error) and PSNR (peak signal-to-noise ratio). The shifting method is used to increase the data hiding capacity. In this system, the encryption part embeds the secret image using steganography and the decryption part extracts the original image. By analyzing and comparing the proposed system with the existing system, it is proved that the system proposed was much better than the existing systems.