bluuebunny/crossref_metadata_2025 huggingface dataset:
Sieve algorithms for some orthogonal integer lattices
Authors: Tchinda Arnaud Gires Fobasso , Fouotsa Emmanuel , Jugnia Celestin Nkuimi
Published: August 2022

We propose in this work a Sieve algorithm that we called OrthogonalInteger sieve algorithm for some orthogonal integer lattices and particularly the case of integer lattices [Formula: see text], root lattices of type [Formula: see text] ([Formula: see text]) and of type [Formula: see text] ([Formula: see text]). In these cases, we use the famous [Formula: see text] algorithm to find the shortest vector of these lattices. Indeed, in general, a sieve algorithm builds a list of short random vectors which are not necessarily in the lattice, and try to produce short lattice vectors by taking linear combinations of the vectors in the list. But in our case, we built a list of short vectors in the lattice. From the first column of the [Formula: see text]-reduced basis of the considered basis, we have the list of at least [Formula: see text] and at most [Formula: see text] short vectors for the general case (where [Formula: see text] is the dimension of the lattice) of orthogonal integer lattices [Formula: see text]. For the lattices [Formula: see text], [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]), we have, respectively, [Formula: see text], [Formula: see text] and [Formula: see text] short vectors. The proposed sieve algorithm for integer lattice [Formula: see text] runs in space [Formula: see text] and the OrthogonalInteger sieve algorithm performs [Formula: see text] arithmetic operations and is polynomial in space.
Efficient prediction of future stock values with Gann square using machine learning algorithm
Authors: Manjunath K. V. , Chandra Sekhar M.
Published: August 2022

Stock market forecast improves the economic development of the country. People invest in the stock market at high risk without performing the appropriate analysis. So, it is essential to identify the stock market value prediction through existing stock market datasets. This analysis can be made through machine learning algorithms. Machine Learning is defined as the process through which only investors will gain the profit when investing the money into the stock market. Many prediction models have been contributed by the existing investigators to estimate the stock value. The contribution by Hybrid Heuristic algorithm is that it provides a coherent future prediction of stock market prices and investors will also get the insights about how to sell and buy a specific stock to get more profit. Gann square technique is used to forecast a price maneuver dependent on value, pattern analysis and period which is also used for intraday online trading without prior knowledge.
A review on key technologies of invulnerability of software-intensive equipment based on complex network
Authors: Yin Shizhuang , Shi Quan , You Zhifeng , Wu Weiyi , Guo Yurong
Published: August 2022

The research on the invulnerability of software-intensive equipment can improve the continuous combat ability of equipment as a necessary means to deepen the understanding of equipment damage law and improve the equipment anti-destruction ability and recovery ability. The general process and key technologies of software-intensive equipment invulnerability research are summarized from component level, equipment level and equipment network level, and the existing problems are analyzed; the future development direction of equipment invulnerability research is prospected, which provides some reference to the research work of software-intensive equipment invulnerability.
Three-Layer Multi-UAVs Path Planning Based on ROBL-MFO
Authors: Oyana Salvador N. Obama , Li Jun , Usman Muhammad
Published: August 2022

This paper proposes a new three-layer path planning method, where we fused two existing path planning methods (global path and local path) into a single problem for multi- unmanned aerial vehicles (UAVs) path planning for UAV. The global-path network layer contains the latest information and algorithms for global planning according to specific applications. The trajectory planning layer represents the kinematics and different motion characteristics, the planning-execution layer implements the local planning algorithm for obstacle avoidance. In the last layer, we propose a new swarm intelligence algorithm called the refraction principle and opposite-based-learning moth flame optimization (ROBL-MFO). In contrast to the classical MFO, the proposed algorithm addresses the shortcoming of the classical MFO algorithm. First, it adapts the moth position update formula to the notion of historical optimal flame average and improves the convergence speed of the algorithm. Second, it utilizes a random inverse learning strategy to narrow down the search space. Finally, the principle of refraction gives the algorithm the ability to jump out of local optima and helps the algorithm avoid premature convergence. The experimental results show that the performance of the proposed algorithm is versatile, robust, and stable.
Geodynamic generation of a Paleocene–Eocene landscape buried beneath North Bressay, North Sea
Authors: Stucky de Quay Gaia , Roberts Gareth G.
Published: August 2022

Histories of vertical lithospheric motions provide important clues about geodynamic processes. We present evidence of an ancient (
c.
58–55 Ma) landscape that probably underwent rapid uplift and subsidence during the initiation of the Icelandic plume. Now buried beneath
c.
0.4–0.8 km of rock in the North Bressay region in the North Sea, this landscape is located within a sedimentary basin on the margin of the North Atlantic Ocean. We use high-resolution 3D seismic reflection data to map this ancient surface. Correlation of stratigraphy with a survey in the Bressay region constrains the age and depositional environment. The landscape contains excellent evidence of meandering fluvial channels, some of which record avulsions, that terminate against a coastline to the east where deltaic landforms are identified. The landscape was depth-converted and decompacted to generate a digital elevation model from which river profiles were extracted. Their geometries indicate that the landscape was generated by three phases of uplift. This history of uplift and subsidence is analogous to similar-aged landscapes in the Judd area
c.
400 km to the west and Bressay
c.
30 km to the south, and appears to be another manifestation of lithospheric motions generated by the passage of warm thermal anomalies away from the Icelandic plume.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment
Authors: Pattnaik Nandita , Li Shujun , Nurse Jason R. C.
Published: August 2022

The security and privacy of smart home systems, particularly from a home user’s perspective, have been a very active research area in recent years. However, via a meta-review of 52 review papers covering related topics (published between 2000 and 2021), this article shows a lack of a more recent literature review on user perspectives of smart home security and privacy since the 2010s. This identified gap motivated us to conduct a systematic literature review (SLR) covering 126 relevant research papers published from 2010 to 2021. Our SLR led to the discovery of a number of important areas where further research is needed; these include holistic methods that consider a more diverse and heterogeneous range of home devices, interactions between multiple home users, complicated data flow between multiple home devices and home users, some less studied demographic factors, and advanced conceptual frameworks. Based on these findings, we recommended key future research directions, e.g., research for a better understanding of security and privacy aspects in different multi-device and multi-user contexts, and a more comprehensive ontology on the security and privacy of the smart home covering varying types of home devices and behaviors of different types of home users.
Similarity of hand muscle synergies elicited by transcranial magnetic stimulation and those found during voluntary movement
Authors: Yarossi Mathew , Brooks Dana H. , Erdoğmuş Deniz , Tunik Eugene
Published: August 2022

This is the first work to examine the similarity of modularity in hand muscle responses to transcranial magnetic stimulation (TMS) of the motor cortex and that derived from voluntary hand movement. We show that TMS-elicited muscle synergies of the hand, measured at rest, reflect those found in voluntary behavior involving finger fractionation. This work provides a basis for future work using TMS to investigate muscle activation modularity in the human motor system.
The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis
Authors: Xu Yanglong , Zou Xue , Mei Jie
Published: August 2022

Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.
Exploration of Regional Public Digital Culture Service Mode Based on Artificial Intelligence Technology
Authors: Yin Qixing , Dong Yifei
Published: August 2022

In order to improve the development of China’s public cultural service supply field, this paper introduces the public culture of weather services with the support of network intelligence services and smart devices. This article examines the purpose of public cultural cloud services to identify current public service public culture and future development models; The results show that the average consistency indexes determined by the weight of evaluation indexes based on AHP are 0, 0, 0.514, 0.894, 1.118, 1.248, 1.344, 1.421, and 1.461, respectively. Their importance order is judged according to the degree of relative importance. According to the hierarchy and diversity of users’ needs, a “multiple” development model of public digital culture cloud service is constructed. This data provides a measure of resistance to innovations in public cloud culture, data advocacy to support public climate change in four areas: the reform of service strategies, the integration of public culture, the development of specialized resources, and the development of service teams and customer capabilities.
Experimental Investigations on the Mechanical Characteristics of Natural Fiber Particle‐Reinforced Polymer Composites under Cryogenic Environment
Authors: Alshgari Razan A. , Anandaram Harishchander , Kistan A. , Nisha N. , Vel V. M. , Anitha G. , Wabaidur Saikh Mohammad , Islam M. A. , Alemayehu Agonafir
Published: August 2022

In the past few years, researchers have turned away from previous metals and minerals and more towards lightweight materials throughout addition to creating lightweight, better effective composites for special tasks. The composite materials were created by employing the most advantageous hand layup approach. To attain the aforementioned goals, the following limitations were implemented: at various levels, (i) coconut shell powder, (ii) walnut powder, and (iii) cryogenic treatment hours are all accessible. At 77 K, the laminated composite was put in a cryogenic chamber. Tensile, flexural, and interlaminar shear strengths were all tested mechanically. In comparison to 100 μm coconut shell powder‐based particle composites, 80 μm walnut shell composites gave the maximum mechanical strength, while 10 wt% coconut shell powder and walnut and 80 wt% polyester also had a beneficial impact. The morphological behaviour of the particulate material was detected using a scanning electron microscope (SEM).
Ethyl Acetate Fraction of Hedyotis diffusa Willd Induces Apoptosis via JNK/Nur77 Pathway in Hepatocellular Carcinoma Cells
Authors: Ning Weimin , Xu Nishan , Zhou Chunhong , Zou Lifang , Quan Jingyu , Yang Hua , Lu Zinbin , Cao Huihui , Liu Junshan
Published: August 2022

Background. Hepatocellular carcinoma (HCC) is characterized by poor diagnosis and high mortality. Novel and efficient therapeutic agents are urgently needed for the treatment. Hedyotis diffusa Willd (HDW) is used to treat cancers, especially HCC in China. Purpose. The study aimed to identify the main anti-HCC extract in HDW and to explore the mechanism of the active extract. Materials and Methods. The high-performance liquid chromatography-quadrupole-time of flight mass spectrometry (HPLC-QTOF-MS) method was used for the simultaneous determination of main compounds in the ethyl acetate fraction of HDW (EHDW). The toxicity test of different HDW fractions was carried out on larvae at 2 day-post-fertilization (dpf) for 72 h. The in vivo anti-HCC effect of different HDW fractions was evaluated on a zebrafish tumor model by immersion administration. The antiproliferative effect of HDW fractions was determined with MTT assay, as well as hematoxylin and eosin (HE) staining assay. Hoechst 33258 staining was used to observe changes in nucleus morphology. Flow cytometry analysis was used to investigate apoptosis induction. Western blot analysis was used to examine apoptosis-related proteins, and key proteins in JNK/Nur77 signaling pathway. SP600125 was served to validate the apoptotic mechanism. Results. EHDW showed the strongest tumor cell growth inhibitory effect on zebrafish tumor model. Further study revealed that EHDW induced apoptosis in zebrafish tumor model and in cultured Hep3B cells. Meanwhile, it has been shown that the levels of BCL2-associated X (Bax), cytochrome c (cyto c), cleaved-caspase 3, and poly-ADP-ribose polymerase (PARP) cells were upregulated. In contrast, the level of antiapoptotic B cell lymphoma-2 (Bcl-2) was downregulated in Hep3B cells. Additionally, EHDW activated JNK/Nur77 pathway by increasing the levels of p-JNK(Thr183/Tyr185) and p-Nur77(Ser351). Further study showed that blockage of JNK by SP600125 reversed EHDW-induced JNK/Nur77 pathway and the downstream apoptotic proteins. Conclusion. In conclusion, EHDW exerted the anti-HCC effect, which may be attributed to the activation of JNK/Nur77 pathway. This study supported the rationale of HDW as an HCC therapeutic agent.
Lightweight Deep Learning Model for Marketing Strategy Optimization and Characteristic Analysis
Authors: Su Yang , Wang Chonghong , Sun Xuejiao
Published: August 2022

The business model of traditional market is declining day by day, and people’s consumption cognition has risen to a new level with the leap in science and technology. Enterprises need to adjust and optimize their marketing strategies in time according to the new consumption characteristics, so as to smoothly adapt to the environmental changes in the Internet age. This paper briefly analyzes the relationship between sales development and psychology and constructs a fusion model that can predict preferences with the help of neural network structure of the deep learning method. Describe user portraits and characteristics, analyze users’ purchasing behavior and credit literacy, and push related products combined with a hash algorithm to achieve accurate e-commerce marketing purposes. The results show that (1) the model constructed in this paper and five different models are used for multi-modal recognition analysis: the accuracy is 79.56%, the recall rate is 77.43%, F1 is 0.785, and the error value can be reduced to about 0.18 by epoch iteration; the model is superior and has great use value. (2) Using the model to extract user attribute features and predict certain preferences, 13 topics and weight ratios are obtained for users of a certain platform, and the portrait model of each user is constructed. (3) According to the portrait optimization, 8 different marketing strategies are obtained, and the marketing effect is remarkable, fluctuating between 69% and 82%, and the income situation is also satisfactory. The final model design is reasonable and the data performance is good, which provides an intelligent and efficient dynamic strategy service for enterprises.
A Hierarchical Children’s Dance Movement Pose Estimation Method Based on Sequence Multiscale Feature Fusion Representation
Authors: Qin Yanan , Huang Tao , Tang Guanzhen
Published: August 2022

Aiming at the problem that traditional human motion pose estimation methods cannot accurately capture and estimate the movement changes of children dancers, a hierarchical dance pose estimation method for children based on sequence multiscale feature fusion representation is proposed. By comparing the pose feature extraction algorithm with the actual recognition effect, the recognition rates of the dancer’s upper body, infiltration, and whole body have increased by 14.2, 10.6, and 12.6, respectively. The experimental results show that the proposed pose estimation algorithm achieves good pose estimation results on both the standard human pose estimation dataset and the self-built dance dataset.
Astrocytic IGF-1 and IGF-1R Orchestrate Mitophagy in Traumatic Brain Injury via Exosomal miR-let-7e
Authors: Dabin Ren , Wei Chen , Liang Shu , Ke Cao , Zhihan Wang , Ping Zheng
Published: August 2022

Defective brain hormonal signaling and autophagy have been associated with neurodegeneration after brain insults, characterized by neuronal loss and cognitive dysfunction. However, few studies have linked them in the context of brain injury. Insulin-like growth factor-1 (IGF-1) is an important hormone that contributes to growth, cell proliferation, and autophagy and is also expressed in the brain. Here, we assessed the clinical data from TBI patients and performed both in vitro and in vivo experiments with proteomic and gene-chip analysis to assess the functions of IGF-1 in mitophagy following TBI. We show that reduced plasma IGF-1 is correlated with cognition in TBI patients. Overexpression of astrocytic IGF-1 improves cognitive dysfunction and mitophagy in TBI mice. Mechanically, proteomics data show that the IGF-1-related NF-κB pathway transcriptionally regulates decapping mRNA2 (Dcp2) and miR-let-7, together with IGF-1R to orchestrate mitophagy in TBI. Finally, we demonstrate that brain injury induces impaired mitophagy at the chronic stage and that IGF-1 treatment could facilitate the mitophagy markers via exosomal miR-let-7e. By showing that IGF-1 is an important mediator of the beneficial effect of the neural-endocrine network in TBI models, our findings place IGF-1/IGF-1R as a potential target capable of noncoding RNAs and opposing mitophagy failure and cognitive impairment in TBI.
Effect of Electroacupuncture on Short-Chain Fatty Acids in Peripheral Blood after Middle Cerebral Artery Occlusion/Reperfusion in Rats Based on Gas Chromatography–Mass Spectrometry
Authors: Ke Xiaohua , Xiang Qing , Jiang Pingli , Liu Weilin , Yang Minguang , Yang Yihan , Shi Dan , Chen Lidian , Tao Jing
Published: August 2022

Previous fundamental and clinical research has shown that electroacupuncture (EA) at the acupoints of Quchi (LI11) and Zusanli (ST36) can successfully alleviate motor dysfunction following stroke. Additionally, it has been discovered that gut microbiota and their metabolites play an essential role in stroke. However, the relationship between the metabolites of gut microbiota and the efficacy of EA is still unclear. Therefore, the aim of this study was to evaluate the mechanism of EA at LI11 and ST36 in the treatment of motor dysfunction after middle cerebral artery occlusion/reperfusion (MCAO/R) in model rats by comparing the differences and correlation between different short-chain fatty acids (SCFAs) and the recovery of motor function. The results indicated that EA at LI11 and ST36 acupoints enhanced the neurological function, motor function, and infarct volume of MCAO/R rats. The levels of acetic acid, propionic acid, and total SCFAs were considerably lower in the MCAO/R group than in the sham group (

P
<
0.05

). Acetic acid, propionic acid, and total SCFA concentrations were substantially higher in the MCAO/R + EA group than in the MCAO/R group (

P
<
0.05

). Finally, Pearson correlation analysis revealed that the propionic acid concentration was substantially favorably connected with the duration on the rotarod (

r
=
0.633

and

P
<
0.05

) and highly negatively correlated with the modified neurological severity score (mNSS) (

r
=
−
0.698

and

P
<
0.05

) and the percentage of cerebral infarct volume (

r
=
−
0.729

and

P
<
0.05

). Taken together, these findings indicate that the increase in propionic acid may be one of the mechanisms and targets of EA at LI11 and ST36 acupoints to improve poststroke motor dysfunction in MCAO/R rats.
Safe Operation Management of Urban Smart Grid Based on Deep Learning
Authors: You Huimin
Published: August 2022

The smart grid in the twenty-first century is constantly innovated by the big data technology and the Internet of Things (IoT) technology. As the second generation of a power network, the smart grid keeps developing towards automation and intelligence, driving the energy conversion rate, power utilization rate, and energy supply rate to increase. However, in the smart grid, the power terminal has a pivotal role in controlling, monitoring, and regulating the production process of electricity, which is currently facing many security challenges. The most critical aspect of smart grid security management is to ensure the security of power terminals. Existing solutions generally monitor power terminal devices by monitoring power terminal traffic; however, such security policies can only monitor attacks with characterization properties at the traffic level and cannot be used to monitor power terminal devices directly. Based on this, this paper reviewed the literature on intelligent operation and maintenance and deep learning at home and abroad and comprehensively analyzed the research progress of intelligent operation and maintenance, and in the comparative analysis of deep learning methods, because the convolutional neural network has fewer connections and parameters and can control the capacity by controlling its depth and width, it is convenient to establish a model with larger learning capacity, so a convolutional neural network is chosen for data analysis. In this study, we choose to use the convolutional neural network to analyze the data, combine the monitoring, management, and fault location of operation and maintenance work organically through some deep learning algorithms, reduce the number of model layers through a deep learning-based security monitoring technology for electric power terminals to improve the training speed and efficiency, and achieve all-round protection for electric power terminals at the device level and the network level. The management of urban smart grid dispatching operation also requires strict implementation of relevant technical standards to ensure the standardized operation and enhance the safety and stability of grid operation.
Small Airway Dysfunction in Chronic Bronchitis with Preserved Pulmonary Function
Authors: Ding Qi , Mi Bai-Bing , Wei Xia , Li Jie , Mi Jiu-Yun , Ren Jing-Ting , Li Rui-Li
Published: August 2022

Impairment of pulmonary function was evaluated in chronic bronchitis patients with preserved ratio impaired spirometry (PRISm). We retrospectively collected clinical data from 157 chronic bronchitis (CB) and 186 chronic obstructive pulmonary disease (COPD) patients between October 2014 and September 2017. These patients were assigned to three groups: control (normal pulmonary function), PRISm (forced expiratory volume in 1 second [FEV1]/forced vital capacity [FVC] ≥ 0.7, FEV1 < 80% of predicted value), and COPD (FEV1/FVC <0.7) groups. Because small airway function was the main focus, in the COPD group, only patients in accordance with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1 and 2 were included. Evaluation of pulmonary function (including impulse oscillometry) was performed and compared among these groups. Compared with the control group, the PRISm and COPD groups showed statistically significant differences in the predicted FEV1%

p
<
0.001

, maximal expiratory flow (MEF) 25%

p
<
0.001

, MEF50%

p
<
0.001

, maximal midexpiratory flow (MMEF) 25–75%

p

<

0.001

, residual volume (RV)/total lung capacity (TLC;

p
<
0.001

), FVC%

p
<
0.001

, total respiratory resistance and proximal respiratory resistance (R5-R20;

p
<
0.001

), respiratory system reactance at 5 Hz (X5;

p
<
0.001

), resonant frequency (Fres;

p
<
0.001

), and area of reactance (Ax;

p
<
0.001

). However, the predicted FEV1% and RV/TLC were similar between the PRISm and COPD groups (

p
=
0.992

and 0.122, respectively). PRISm is a nonspecific pattern of pulmonary function that indicates small airway dysfunction and may increase the risk of transformation to obstructive ventilation dysfunction. This trial is registered with ChiCTR-OCH-14004904.
Substation Equipment Temperature Prediction Method considering Local Spatiotemporal Relationship
Authors: Sun Lijie , Chen Shuang , Zhu Junfei , Li Jianhua
Published: August 2022

Temperature prediction of substation equipment is one of the important means for intelligent inspection of substation equipment. However, there are still three challenges: (1) Limited extracted samples; (2) Typical nonlinearity, seasonality, and periodicity; (3) Changes in equipment and working conditions. To solve the problems above, a substation equipment temperature prediction method considering Spatio-temporal relationship (SETPM-CLSTR) is proposed. First, according to the time series of equipment temperature from two aspects of temporal and spatial, it is determined that the equipment temperature has seasonal, temporal, and spatial correlation; second, aiming at the problem that the spatial location correlation cannot be described quantitatively, grey relational analysis (GRA) is adopted to determine the spatial location monitoring points closely related to the prediction target; then, the daily maximum temperature and daily minimum temperature from the environment, the predicted target temperature from the past several times in time and the temperature from the spatial location monitoring point with close correlation in space are constructed as Spatio-temporal feature vectors; finally, CNN-BiLSTM double-layer depth network model is proposed to predict the equipment temperature. SETPM-CLSTR has applied to temperature prediction of phase A contact from primary equipment of a substation in Taizhou City, Zhejiang Province. Under the two prediction performance evaluation indexes of MASE and RMSE, compared with three correlation models of LSTM, BiLSTM, and CNN-LSTM from two aspects of different features and models, it is verified that SETPM-CLSTR in this study has better prediction performance.
[Retracted] Risk Prediction Method of Obstetric Nursing Based on Data Mining
Authors: Jin Deyan
Published: August 2022

Obstetric nursing is not only complex but also prone to risks, which can have adverse effects on hospitals. Improper handling of existing risks in obstetric care can lead to enormous harm to patients and families. Therefore, it is necessary to pay attention to the risks of obstetric nursing, especially to predict the risks in a timely manner, and take effective measures to prevent them in time, so as to achieve the purpose of allowing patients to recover as soon as possible. Data mining has powerful forecasting function, so this paper proposes to combine the data‐mining‐based support vector machine method and XGBoost method into a forecasting model, which overcomes the shortcomings of unstable forecasting and low accuracy of a single forecasting model. The experimental results of this paper have shown that the prediction accuracy of the SVM‐XGBoost combined prediction model has reached 100%, the accuracy of the single SVM prediction model is about 78%, and the accuracy of the single XGBoost prediction model is about 75%. Compared with the single SVM model and the XGBoost prediction model, the accuracy rate had increased by about 22% and 25%, and the precision rate and recall rate are also improved. Therefore, it is very suitable to use the SVM‐XGBoost combined prediction model to predict the risk of obstetric nursing.
[Retracted] Effects of the Wnt/β‐Catenin Signaling Pathway on Proliferation and Apoptosis of Gastric Cancer Cells
Authors: Chen Jia , Wang Xingyu , Zhang Jianlin , Chang Jiawei , Han Chuanjun , Xu Zhouwei , Yu Hongzhu
Published: August 2022

Objective. To explore the effect of the Wnt/β‐catenin signaling pathway on the proliferation and apoptosis of gastric cancer cells. Methods. An MTT colorimetric assay was used to detect the inhibitory effect of the Wnt/β‐catenin signaling pathway inhibitor FH535 on the proliferation of MKN45 gastric cancer cells. The cell proliferation index (PI) and apoptosis index (AI) were measured by flow cytometry. The expression levels of β‐catenin, c‐myc, and cleaved caspase‐3 in MKN45 gastric cancer cells were detected. Results. After using the Wnt/β‐catenin signaling pathway inhibitor FH535, MKN45 gastric cancer cells showed obvious shrinkage, death, and cell density decrease. MTT showed that the A value of MKN45 gastric cancer cells in FH535 group was significantly lower than that in the control group (P < 0.05). The survival rate of MKN45 gastric cancer cells in FH535 group was significantly lower than that in the control group (P < 0.05). The cell cycle of gastric cancer was arrested in G0/G1 phase. The expression levels of β‐catenin and c‐myc protein in MKN45 gastric cancer cells in FH535 group decreased significantly (P < 0.05), while the expression level of cleaved caspase‐3 protein increased significantly (P < 0.05). Conclusion. The Wnt/β‐catenin signal molecule can maintain the proliferation of gastric cancer cells. Inhibition of the Wnt/β‐catenin signaling pathway can inhibit the proliferation of gastric cancer cells and promote the apoptosis of MKN45 gastric cancer cells.
[Retracted] Clinical Effect of the Guizhi Shaoyao Zhimu Decoction in the Treatment of Hyperuricemia
Authors: Yang Qilin , Zhang Jikong , Li Jiuwei
Published: August 2022

Traditional Chinese Medicine (TCM) is a medical system with a distinctive theoretical framework and extensive experience in identification and treatment acquired by the Chinese people in long‐term medical practice and life practice. It is a complete, integrated, and complex knowledge system in epistemology. This study is aimed at exploring the clinical effectiveness of TCM called the Guizhi Shaoyao Zhimu Decoction in the treatment of hyperuricemia. A total of 100 patients with hyperuricemia at the Medical College of the Second Clinical College, Shandong, China, from January 2019 to January 2022 are selected as the research subjects and divided into group A and group B according to the random table method, with 50 cases in each group. Group A is treated with oral allopurinol tablets, 100 mg, 2 times a day, and group B is treated with the modified Guizhi Shaoyao Zhimu Decoction based on group A. For observation, serum uric acid (SUA) levels, urinary uric acid (UUA) levels, levels of serum inflammatory response factors (IL‐6, CRP, and TNF‐α), vascular endothelial function indexes (serum malondialdehyde (MDA) content, nitric oxide (NO) content), an acute attack of gout, and the incidence of adverse reactions are measured. Results show that after 2 w and 4 w of treatment, the levels of blood uric acid in each group gradually decreased compared with those before treatment, and group B is lower than group A (P < 0.05). After treatment, the vascular endothelial function indexes and inflammatory factor levels in each group are significantly improved compared with those before treatment, and the indexes in group B are better than those in group A. There is no significant difference in the incidence of related adverse reactions and acute attack of gout (P > 0.05). This shows that the TCM Guizhi Shaoyao Zhimu Decoction has a significant curative effect in the treatment of patients with hyperuricemia, which is worthy of clinical reference application.
Student’s Physical Health Prediction Model Based on the Deep Neural Network
Authors: Li Guomin , Hao Linlin
Published: August 2022

The arrival of the Internet of Things era has brought us a series of conveniences, but it is also devouring the physical and mental health of most of us. Especially students, as the future main force of the motherland, their health cannot be ignored. To more accurately and appropriately predict the physical health of students, this paper establishes a deep convolutional neural network (CNN) model and uses its own strong function mapping ability, and using the general physical index of students’ physical health to obtain the total score as the input parameter and the total score of physical health as the output parameter, so as to establish a deep CNN prediction model for students’ physical health. The model firstly overcomes the shortcoming of singleness brought by a simple NN, and it more accurately and clearly reflects the relationship between various physical measurement indicators and the overall physical health score. Secondly, the specific ROC curve and the R-P curve are obtained by comparing the traditional gray EGM prediction model. Finally, from the comparison results of the R-P curve, it can be seen that the AUC of the deep convolutional network is 0.98, while the AUC of the gray EGM prediction model is only 0.89, which shows that the data of the deep convolutional network model is more accurate.
Inactivating Food Microbes by High-Pressure Processing and Combined Nonthermal and Thermal Treatment: A Review
Authors: Nema Prabhat K. , Sehrawat Rachna , Ravichandran Chandrakala , Kaur Barjinder Pal , Kumar Anit , Tarafdar Ayon
Published: August 2022

High-pressure processing (HPP) is a mild technology alternative to thermal pasteurization and sterilization of different food products. HPP has emerged to provide enormous benefits to consumers, i.e., mildly processed food and additive-free food. It effectively retains bioactive compounds and extends the shelf life of food commodities by inactivating bacteria, yeast, mold, and virus. The limitation of HPP in inactivating spores can be overcome by using other thermal and nonthermal processing sequentially or simultaneously with HPP. This review summarizes the applications of HPP in the fruits and vegetables, dairy, meat, fish, and poultry sector. It also emphasizes microbial food safety and the effectiveness of HPP in the load reduction of microorganisms. Comprehensive information about the synergistic effect of HPP with different techniques and their effectiveness in ensuring food safety is reported. The summarized data would be handy to interested researchers and industry personnel.
Application Research of Distributed UAV Nest Based on Multi-Information Fusion Sensor in the Field of Communication
Authors: Cai Lin
Published: August 2022

For the long delay in the communication field of the unmanned robot, which seriously affects the accurate evaluation of the operation status of the UAV, this paper introduces the multi-information fusion sensor into the field of distributed UAV nest communication. By using the multi-information fusion sensor to fuse the attitude data information of the UAV flight, the delay of the information obtained in the communication process is reduced. The multi-information fusion sensor needs to fully consider the delay problem in the communication field during the use of the UAV nest. By using the acceleration data information to make up for the shortcomings of the delay, relatively accurate and high-precision UAV motion evaluation results can be obtained. According to the simulation of the UAV using the multi-information fusion sensor during the flight, the method proposed in this paper is verified on the UAV system. After comparing the simulation and test results, it can be seen that the method used in this paper can effectively improve the application efficiency of multi-information fusion sensors in the communication field of distributed UAV nests, and has a high reference value for data information analysis.
Research on the Bolt Positioning System Based on Multieye Vision Industrial Robots
Authors: Kong Jun , Li Qiang , Yin Yi , Li Cen , Luo Wei
Published: August 2022

In order to improve the accuracy of bolt positioning for industrial robots, this paper studies the bolt positioning system of industrial robots combined with multieye vision technology. This paper introduces three reconstruction algorithms ART, SIRT, and SART from the perspective of theory and implementation. Moreover, the projection matrix calculated with 0, 1 weighting, length weighting, and linear interpolation weighting is used for three reconstruction algorithms to carry out reconstruction experiments. In addition, this paper combines the actual working conditions of bolt positioning to construct the system of this paper and conducts system simulation research combined with the working conditions of the robot. The research shows that the bolt positioning system based on the multieye vision industrial robot proposed in this paper has a good performance in bolt positioning.
Did the Inclusion of China’s A-Shares in the MSCI Index Improve the Information Content of Listed Firms? Analysis Based on Stock Price Synchronisation and Environmental Social Governance
Authors: Wu Jing
Published: August 2022

Chinese A-shares were officially included in the Morgan Stanley Capital International (MSCI) Emerging Markets Index from June 2018. The inclusion of the A-share market into the MSCI Index is one of the most influential events in the opening up of the capital market of China. However, China’s A-share market has an imperfect system compared with that of developed countries. Stock price synchronisation is more serious in China than in developed countries, and Environmental Social Governance (ESG) disclosures are imperfect. Excessive stock price synchronicity can affect the information content of stock prices, and imperfect ESG disclosures are not conducive to the investment decisions of investors and thus not conducive to the price mechanism for adjusting the market. From the perspective of stock price synchronisation and ESG disclosure, this study discusses the impact of the exogenous event of the inclusion of A-shares in the MSCI Index on China’s capital market. Based on data of listed firms from 2011 to 2019, this study determines whether MSCI target stocks have high stock price information content and explores the ESG disclosure level based on a difference-in-differences (DID) model. In addition, this study conducts a parallel trend hypothesis test and propensity score matching (PSM) to test the robustness of the results. Empirical results show that the inclusion of A-shares in the MSCI Index increased the stock price information content and reduced the stock price synchronisation of MSCI target stocks. At the same time, this study tests the quality of the ESG information disclosure of A-shares after their inclusion in the MSCI Index and finds that the level of disclosure of the underlying stocks improved. This finding indicates that the inclusion of A-shares in the MSCI Index plays a role in improving the quality of ESG disclosure through information channels. The research conclusions have important implications for the active promotion of the expansion of the MSCI Index and further opening up of the capital market of China. In addition, the conclusions can provide a reference for subsequent MCSI decision-making and insights into the capital markets of other emerging-market countries.
Fault Diagnosis Algorithm of Gearbox Based on NAPSO-VMD Self-Adaptive Noise Reduction and Dual-Sensor Feature Fusion
Authors: Liu Yangshuo , Kang Jianshe , Guo Chiming , Bai Yunjie , Meng Shuo
Published: August 2022

Condition monitoring plays a very important role in equipment fault diagnosis technology. However, existing monitoring methods often collect equipment fault signals from a single dimension, resulting in a major lack of fault information. To improve the problem, we built a gearbox preset fault test bench and constructed a dual-sensor acquisition system to realize the multiple dimensions of vibration signal acquisition in the horizontal and vertical directions of the gearbox. At the same time, given the poor adaptability of most current signal preprocessing methods, the improved nonlinear adaptive inertial weight particle swarm optimization algorithm (NAPSO) and variational modal decomposition (VMD) are combined to optimize the key parameters in VMD with the maximum correlation kurtosis convolution (MCKD) as the fitness function. Further, after extracting fault features from the intrinsic mode functions (IMFS) decomposed by VMD, the single-layer sparse autoencoder network (SAE) and the double-layer stacked sparse autoencoder network (SSAE) with different structures are used to realize an effective fusion of multidimensional information and deep feature extraction. Finally, the hybrid fault diagnosis of gearboxes is realized by using the random forest algorithm (RF) as the classifier. The experimental results show that the accuracy of the method proposed in this paper can reach 96.0%, and the accuracy can be improved by 3.0% and 4.0%, respectively, when compared with a single horizontal or vertical sensor signal input.
Hotspot Detection with Machine Learning Based on Pixel-Based Feature Extraction
Authors: Lin Zhifeng , Gu Zhenghua , Huang Zhipeng , Bai Xiqiong , Luo Lixuan , Lin Geng
Published: August 2022

The complexity of physical verification increases rapidly with fast shrinking technology nodes. Considering only design rule checking (DRC) constraints or lithography models cannot capture the side physical effects in the fabrication process well. Thus, it is desirable to consider a more general physical verification problem with various types of hotspots. In this paper, we apply machine learning which is based on pixel-based feature extraction to deal with the generalized hotspot detection problem. First, a two-dimensional discrete Fourier transformation-based pixel extraction method is proposed to alleviate the shifting effect and produce stable hotspot features. Then, a pattern-based layout scanning approach is developed to enhance the program efficiency while preserving good detection accuracy. Finally, we design two false alarm reduction strategies to effectively reduce the number of detected nonhotspots and further improve the accuracy of hotspot position. Experimental results based on the industrial benchmarks show that our algorithm outperforms three competitive works in terms of accuracy, false alarm rate, efficiency, and time.
Extraction, Characterization, and Structure of a Novel Heteropolysaccharide from Lepidium sativum and Its Effects on Wound Healing in Diabetic Rats
Authors: Ben Slima Sirine , Ktari Naourez , Chouikhi Aicha , Hzami Amina , Bardaa Sana , Trabelsi Imen , Ben Salah Basma , Ben Salah Riadh
Published: August 2022

The present study undertakes the extraction of a novel polysaccharide from Lepidium sativum (PLS) and the determination of its physicochemical composition and antioxidant properties, as well as its potential wound healing activity in alloxan‐induced diabetic rats. This polysaccharide presented a lighter natural color, whose luminosity (L∗), red‐green intensity (a∗), and blue‐yellow intensity (b∗) were recorded at 63.26, 5.87, and 27.28, respectively. The PLS was structurally characterized by Fourier transform infrared (FT‐IR) spectroscopy, UV spectrum, high performance liquid chromatography (HPLC), gas chromatography (GC), nuclear resonance magnetic (NMR), and high‐pressure gel filtration chromatography. The FT‐IR and UV spectra showed the characteristic band of polysaccharides. According to HPLC, the crude PLS is a heteropolysaccharide composed of glucose, xylose, and galactose. Results obtained by 1H NMR indicated that PLS consisted of three monosaccharide residues with α and β anomers. This novel polysaccharide had an average molecular weight of 98.51 kDa and displayed potential antioxidant activities determined through three different assays: scavenging activity against 2,2′‐azino‐bis‐3‐ethylbenzothiazoline‐6‐sulphonic acid (ABTS), 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) scavenging assay, and reducing power. These results strongly support the beneficial effects of the PLS to accelerate wound healing in diabetic rats. Indeed, its application significantly increased wound contraction percentage (98 ± 1.11%) after 14 days of experiment. Furthermore, the histological assessment of the PLS‐treated group demonstrated complete reepithelialized wounds by accelerating collagen synthesis. In general, the findings affirmed that PLS is efficient on wound closure in alloxan‐induced diabetic rats.
Fair Transmission of Individual Signals and Formation of Mainstream Information: Evidence from Herd Behaviours in Emergencies
Authors: Wu Xintong
Published: August 2022

Risk society is full of emergencies, accompanied by uncertainties and losses. Under emergencies, controlling herd behaviour is challenging due to more interactions and changes among individuals. This research establishes Bayes conditional probability models to explain the fair transmission of individual signals and individual decision-making after receiving others’ signals. The simulation shows the following conclusions: first, each individual has a fair chance to influence the mainstream information; second, the order in which individuals make decisions during an emergency affects the difficulties and likelihood of making a rational decision; third, the high authority of information can become mainstream and guide individual behaviour; and fourth, two individual characteristics, including risk appetite and personal experience, are important in the fair transmission of individual signals and formation of mainstream information. According to the findings, this research proposes two strategies, including interfering with information and controlling existing key opinion leaders to control the mainstream information within a group in emergencies. These two strategies are proved to be useful in detecting and preventing approaches to alleviate individual herd behaviour, which should be monitored and controlled in machine learning models for individual behaviour simulation and prediction. Compared to previous research that focuses on media and public opinion in emergencies, this research focuses on a specific type of information (i.e., individual decision-making and actions) on the individual level and its effects on herd behaviours within the group. This research complements the explanation of the micro-mechanism of how individuals receive information and make decisions and actions.