bluuebunny/crossref_metadata_2025 huggingface dataset:
Teaching Effect Evaluation System of Ideological and Political Teaching Based on Supervised Learning
Authors: Tian Yihao
Published: February 2022

University students face immense challenges in current situations in ideological and political research. Therefore, the way ideological work constantly needs to be adapted, and the exchange of advanced experience strengthened to increase ideological and political education (IPE) in universities. Specific methods of university administration may include only ideological and political courses. Courses information and student grades did not conduct an ideological or political evaluation of the student. They assessed the psychological behaviors of the student based on their success, nor did them include clear information on the course schedule for specific ideological and political courses. This article, Supervised learning-based teaching evaluation approach (SL-TEA), has been proposed to focus on supervised learning from ideas about machine learning technology and the current IPE status, to be developed using a brief analysis procedure. Supervised learning uses a practice set to provide the necessary quality through teaching models. Inputs and correct outputs that allow the model to learn over time are part of this training data. The study uses the system of experts to manage, operate and monitor model evaluation data and create a related database for a real-time update. Besides, to check the impact of the model and to run simulation tests. This study SL-TEA model follows the real needs of the system that the ideological and political teaching content of colleges and colleges can be evaluated. Thus, the experimental results show the better performance through the highest student accuracy ratio of 97.1 %, a high-performance ratio of 94.3%, improved political thinking rate of 92.8%, improved actual positive rate of 90.2%, the false-positive rate of 92.2%, enhance learning rate of 96.6% and reduce the error rate 21.2%, compared to other methods.
Study of a Novel Nonlinear Viscoelastic Bio-Inspired Multi-Dimensional Vibration Isolation Device
Authors: He Zhen-Hua , Xu Zhao-Dong , Xue Jian-Yang , Jing Xing-Jian , Dong Yao-Rong , Li Qiang-Qiang
Published: February 2022

This study proposes a novel viscoelastic bio-inspired multi-dimensional vibration isolation device (VBM-VI). The viscoelastic (VE) damper and the limb-like-structure (LLS) are first installed into the vibration device to make it simultaneously reveal excellent vibration isolation performance at three directions. First, the dynamic equations of the proposed device are established, and the significant coupling nonlinear characteristics caused by material and geometric factor together are discussed. Then, the harmonic balance method (HBM) is used to solve the nonlinear dynamic equation, and the geometrical parameters of the VE damper and LLS device are studied to assess the influence of coupling nonlinear characteristic on the vibration isolation performance. The results indicate that the coupling nonlinear characteristics have an excellent advantage on vibration control, and the proposed VBM-VI device can achieve different vibration isolation performances by adjusting the parameters of the VE damper or LLS. The study also shows that the vibration isolation performances at the horizontal and vertical directions are coupled and restricted with each other, showing different levels of sensitivity to parameter variation. Finally, a comparative study is performed between the traditional vibration isolation device (TV-VI) and the VBM-VI device, indicating that the coupling nonlinear characteristic caused by the VE damper and LLS possesses remarkable advantage of improving the vibration isolation performance of the VBM-VI device.
Compact Grid-Characteristic Scheme for the Acoustic System with the Piece-Wise Constant Coefficients
Authors: Golubev Vasily , Shevchenko Alexey , Khokhlov Nikolay , Petrov Igor , Malovichko Mikhail
Published: February 2022

This paper considers numerical simulation of acoustic waves in heterogeneous two-dimensional (2D) media by the grid-characteristic method. We present a novel high-order compact numerical scheme that accurately handles discontinuous material parameters. We present one-dimensional and 2D formulations and discuss their programming implementation. The merits of this approach are evaluated in numerical experiments. It is verified empirically that the scheme demonstrates the second-order of convergence. Furthermore, we apply the scheme to the Sukhoi Log gold deposit model and the Marmousi model. The primary practical outcome of this research is that it provides a tool for precise simulation of wavefields in complex media with discontinuous parameters.
Analytical Approach to the Coupled Effects of Slope Angle and Seepage on Shallow Lined Tunnel Response
Authors: Zhao Nannan , shao Zhushan , Yuan Bo , Chen Xinyuan , Wu Kui
Published: February 2022

The existing analytical models of shallow lined tunnels pay little attention to the coupled effects of slope angle and seepage, leading to an overestimation of tunnel stability. In order to overcome such a problem, based on the methods of coordinate transformation, complex function and conformal mapping, we attempt to provide an analytical approach to estimate the effective stress and deformation in shallow lined tunnels considering the corresponding influence factors in this study. First, the stress function is extended to the Laurent series in the complex plane, followed by Laplace transform and inverse transform methods on stress function, and the analytical solutions for stress and displacement fields around the tunnel are achieved during different stages. Then, Mein–Larson formula and G–A formula are used to obtain the pore water pressure and effective stress of surrounding rock and lining, considering the effects of rainfall and groundwater. The analytical solution provided in this study can be reduced without considering the lining support and slope angle effects. Furthermore, the rationality and effectiveness of the proposed solution are achieved by a comparison of numerical results. Finally, a comprehensive parametric investigation based on the proposed solution is performed, including buried depth, slope angle and rainfall intensity. The results show that the small change of stress and deformation of the ground surface is observed as the buried depth becomes more than 30[Formula: see text]m. If the slope angle varies within a range from 0 to 30[Formula: see text], the influence of slope angle change on the stress and deformation of surrounding rock can be generally neglected. When the rainfall intensity is greater than a certain threshold or rainfall time is shorter than a value, these two parameters are in a limited effect on the seepage change.
Comparison of the EMWA Performance of Nickel Cored and Hollow Carbon Onions
Authors: Li Zhigang , Zhang Dou , Zhang Lei , Ruan Chao , Zhou Qin , Lian Yongfu
Published: February 2022

Carbon onions (CNOs) are highly desirable in the field of electromagnetic wave absorption (EMWA) due to their onion-like structure, low density and excellent conductivity. Herein nickel encapsulated CNOs were fabricated by arc discharge and then transformed into hollow ones via annealing at 900∘C. The structure and EMWA performance of the hollow and nickel encapsulated CNOs were analyzed in detail to investigate the relationship of their EMWA properties and structure. The better EMWA properties of nickel encapsulated CNOs showed that the core–shell interface contributed more EMMA in comparison to the onion-like configuration.
Stock Price Crash Risk of Customers and Entrepreneurial Performance of Suppliers
Authors: Yao Meifang , Yu Ying
Published: February 2022

Nowadays, stock market crash has become a common phenomenon in which the stock price volatility of customers will inevitably affect supplier companies. Using data of enterprises of Growth Enterprise Market of China covering 2015 to 2019, this paper investigates the influence of stock price crash risk of customers on the entrepreneurial performance of suppliers. Our findings show that the customers’ crash risk leads to a decrease in suppliers’ entrepreneurial performance. This negative effect is more pronounced when the supplier faces with insider sales or good information environment. Further analysis shows that supplier investor sentiment plays a full mediating role in the relationship between customers’ stock price crash risk and suppliers’ entrepreneurial performance. Our study theoretically expands this research in stock price crash risk and corporate performance, and sheds light on the cross-enterprise economic consequences of stock price crash risk and its functional mechanism.
PoSSUM: An Entity-centric Publish/Subscribe System for Diverse Summarization in Internet of Things
Authors: Pavlopoulou Niki , Curry Edward
Published: February 2022

Users are interested in entity information provided by multiple sensors in the Internet of Things. The challenges regarding this environment span from data-centric ones due to data integration, heterogeneity, and enrichment, to user-centric ones due to the need for high-level data interpretation and usability for non-expert users, to system-centric ones due to resource constraints. Publish/Subscribe systems (PSSs) are suitable schemes for large-scale applications, but they are limited in dealing with the data and user challenges. In this article, we propose PoSSUM, a novel entity-centric PSS that provides entity summaries for user-friendly subscriptions through data integration, a novel Density-Based VARiance Clustering (DBVARC) for diverse entity summarization that is parameter-free and partly incremental, reasoning rules, and a novel Triple2Rank scoring for top-k filtering based on importance, informativeness, and diversity. We introduce a novel evaluation methodology that creates ground truths and metrics that capture the quality of entity summaries. We compare our approach with a previous dynamic approach and a static diverse entity summarization approach that we adapted to dynamic environments. The evaluation results for two use cases, Healthcare and Smart Cities, show that when users are provided with less information, their data diversity desire could reach up to 80%. Summarization approaches achieve from 80% to 99% message reduction, with PoSSUM having the best-ranking quality for more than half of the entities by a significant margin. PoSSUM has the highest conceptual clustering F-score, ranging from 0.69 to 0.83, and a redundancy-aware F-score up to 0.95, with cases, where it is almost two times better than the other approaches. PoSSUM takes 50% or less clustering processing time and it performs scoring significantly faster for larger windows. It also has comparable end-to-end latency and throughput values, and it occupies a third of the memory compared to the second-best approach.
Insights into the Role of Silicon and Graphite in the Electrochemical Performance of Silicon/Graphite Blended Electrodes with a Multi-Material Porous Electrode Model
Authors: Jiang Yang , Niu Zhiqiang , Offer Gregory , Xuan Jin , Wang Huizhi
Published: February 2022

Silicon/graphite blended electrodes are promising candidates to replace graphite in lithium ion batteries, benefiting from the high capacity of silicon and the good structural stability of carbon. Models have proven essential to understand and optimise batteries with new materials. However, most previous models treat silicon/graphite blends as a single “lumped” material, offering limited understanding of the behaviors of the individual materials and thus limited design capability. Here, we present a multi-material model for silicon/graphite electrodes with detailed descriptions of the contributions of the individual active materials. The model shows that silicon introduces voltage hysteresis to silicon/graphite electrodes and consequently a “plateau shift” during delithiation of the electrodes. There will also be competition between the silicon and graphite lithiation reactions depending on silicon/graphite ratio. A dimensionless competing factor is derived to quantify the competition between the two active materials. This is demonstrated to be a useful indicator for active operating regions for each material and we demonstrate how it can be used to design cycling protocols for mitigating electrode degradation. The multi-material electrode model can be readily implemented into full-cell models and coupled with other physics to guide further development of lithium ion batteries with silicon-based electrodes.
Tracking the Fate of Excess Li in the Synthesis of Various Liy[Ni1−xMnx]O2 Positive Electrode Materials Under Different Atmospheres
Authors: Liu Aaron , Phattharasupakun Nutthaphon , Väli Ronald , Ouyang Dongxu , Dahn J. R.
Published: February 2022

Various Ni-rich Liy[Ni1−xMnx]O2 (x = ∼0.08, 0.2, 0.5) materials were synthesized with excess Li precursor in oxygen, dry air or air to understand what happens to the excess Li during synthesis. The Li[Ni1−xMnx]O2 components of the synthesized materials were single phase and synthesis in oxygen produced materials with less Ni in the Li layer. Inductively coupled plasma optical emission spectrometry (ICP-OES) and titration experiments on as-prepared samples and samples that were rinsed with water are useful in the determination of the amount of Li lost during heating, the amount of Li taken in by the material during synthesis and the amount of residual Li present in the samples as impurity phases. Materials synthesized in oxygen and dry air lost a similar amount of Li during heating but synthesis in air resulted in more Li loss. Synthesis in oxygen increased the lithium content, y, in the Liy[Ni1−xMnx]O2 materials. Materials with a higher Mn content can take in more Li to form Li-rich materials with larger values of y. From these experiments, the fate of Li can be tracked to heating loss, residual Li as impurity phases or uptake into the material as a function of Mn content and synthesis atmosphere.
Temperature-Pressure Phase Diagram and Possible Pressure-Driven New Electronic Phase in the Polar Metal LiOsO3
Authors: Cheng J.-G. , Zhou J.-S. , Uwatoko Y.
Published: February 2022

LiOsO3 is a strongly correlated metal that undergoes a nonpolar to polar transition at the critical temperature (T
s) of 140 K. Complementary to previous studies of structure, Raman, and resistivity under high pressure (P), here we map out a complete pressure evolution of T
s(P) via high-pressure resistance measurements up to 18.5 GPa by using a low-temperature multianvil apparatus. Our results show that T
s(P) first increases linearly with pressure at a large slope as reported and then levels off gradually at pressures above 10 GPa when approaching room temperature (295 K). Interestingly, we find that the resistance R(T) of LiOsO3 at 18.5 GPa in the polar R3c phase exhibits a distinct temperature profile in comparison with those at lower pressures, signaling the possible occurrence of pressure-driven new electronic phase. The critical pressure for this transition is determined to be P
c ≈ 16.8(1) GPa based on the pressure-dependent resistance measurement at room temperature.
Trypsin cleavage of the β1-adrenergic receptor
Authors: Zhu Jing , Steinberg Susan F.
Published: February 2022

Current concepts regarding the molecular basis for β1AR responses derive from literature predicated on the assumption that β1ARs signal exclusively as full-length receptor proteins. However, we recently showed that β1ARs accumulate as both full-length and N-terminally truncated forms in cardiomyocytes and other cell types and that these distinct β1AR species evoke distinct signaling responses. This manuscript provides novel evidence that β1-adrenergic receptors can be cleaved by trypsin (and presumably other inflammatory serine proteases) and that cell surface β1AR cleavage constitutes a heretofore unrecognized mechanism to alter catecholamine-dependent signaling responses.
Identification of Combinations of Plasma lncRNAs and mRNAs as Potential Biomarkers for Precursor Lesions and Early Gastric Cancer
Authors: Chen Lu , Ge Changhui , Feng Xiuxue , Fu Hanjiang , Wang Shasha , Zhu Jie , Linghu Enqiang , Zheng Xiaofei
Published: February 2022

Patients with gastric cancer (GC) are usually first diagnosed at an advanced stage due to the absence of obvious symptoms at an early GC (EGC) stage. Therefore, it is necessary to identify an effective screening method to detect precursor lesions of GC (PLGC) and EGC to increase the 5-year survival rate of patients. Cell-free RNA, as a biomarker, has shown potential in early diagnosis, personalised treatment, and prognosis of cancer. In this study, six RNAs (CEBPA-AS1, INHBA-AS1, AK001058, UCA1, PPBP, and RGS18) were analysed via real-time quantitative polymerase chain reaction (RT-qPCR) using the plasma of patients with EGC and PLGC to identify diagnostic biomarkers. The receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic accuracy. Among the six RNAs, four lncRNAs (CEBPA-AS1, INHBA-AS1, AK001058, and UCA1) were upregulated and two mRNAs (PPBP and RGS18) were downregulated in the plasma of patients with PLGC and EGC. According to the findings of the ROC analysis, the four-RNA combination of INHBA-AS1, AK001058, UCA1, and RGS18 had the highest area under the curve (AUC) value for determining risk of GC in patients with PLGC and the six-RNA combination including CEBPA-AS1, INHBA-AS1, AK001058, UCA1, PPBP, and RGS18 had the highest AUC value for determining the risk of GC in patients with EGC. The results suggest the potential usefulness of noninvasive biomarkers for the molecular diagnosis of GC at earlier stages.
T Lymphocyte Infiltration in Association with IDO1 Expression in Resected Lung Adenocarcinoma and Normal Adjacent Lung Tissues
Authors: Zhao Xiaoling , Li Yaran , Yang Xu , Zhang Xiaochong , Xie Jing , Li Shaoteng , Liu Hongzhen , Guo Jun , He Lili , Chen Wei , Liu Dengxiang
Published: February 2022

Background. Indoleamine 2,3‐dioxygenase 1 (IDO1) catalyzes the first step of tryptophan catabolism in the kynurenine (Kyn) pathway. IDO1 downregulates natural killer cell receptors, and by mechanism, tumor cells escape immune surveillance. Methods. IDO1 protein and mRNA were assessed by immunohistochemistry, immunoblotting, and PCR in the 68 resected lung adenocarcinomas at stages I–III as well as adjacent normal lung tissues. Infiltration of CD3, CD8, and CD4 lymphocytes in the tumor and adjacent normal lung tissues was assessed by immunohistochemical staining. Results. IDO1 protein and mRNA were detected in various stages of lung adenocarcinoma with highest expression at stage III. In contrast, biomarkers of T cell subset, CD3, CD4, and CD8, were highly expressed in the normal lung tissues and stage I adenocarcinoma tissues but significantly reduced in the stage II and III tumor tissues. Conclusions. The current study demonstrated that the higher level of IDO1 expression in the lung adenocarcinoma was, the less infiltration of T lymphocytes was found in the tumors. Findings of this study indicated that IDO1 may contribute to the reduction of T lymphocyte infiltration into the lung adenocarcinoma.
Research on Moving Target Detection and Tracking Technology in Sports Video Based on SIFT Algorithm
Authors: Mei Zhu , Wang Yue
Published: February 2022

Sports video moving target detection and tracking play an important role in enhancing the popularity of sports and the promotion of sports events. This paper combines the SIFT algorithm to carry out the research of sports video moving target detection and tracking technology, to identify sports features, and to improve the sports feature detection algorithm. Moreover, this paper divides the point cloud data into multiple cube grids under the coordinate system where it is located, and then finds the center of gravity of the data points in each grid, and replaces the coordinates of all points in the grid with the coordinates of the center of gravity. In addition, this paper combines data analysis to verify the algorithm and build a sports video moving target detection system. The experimental research results verify that the sports video target detection and tracking technology based on the SIFT algorithm proposed in this paper has good results.
Bayesian Adaptive Lasso for Regression Models with Nonignorable Missing Responses
Authors: Zhao Yuanying , Duan Xingde
Published: February 2022

The main purpose of this article is to develop a Bayesian adaptive lasso procedure for analyzing linear regression models with nonignorable missing responses, in which the missingness mechanism is specified by a logistic regression model. A sampling procedure combining the Gibbs sampler and Metropolis‐Hastings algorithm is employed to obtain the Bayesian estimates of the regression coefficients, shrinkage coefficients, missingness mechanism models parameters, and their standard errors. We extend the partial posterior predictive p value for goodness‐of‐fit statistic to investigate the plausibility of the posited model. Finally, several simulation studies and the air pollution data example are undertaken to demonstrate the newly developed methodologies.
Average and Maximum Papilla Heights around Dental Implants in the Anterior Maxillary Region: A Retrospective Clinical Study
Authors: Gholami Gholam Ali , Hariri Soheil , Amid Reza , Roghanizadeh Leyla , Kadkhodazadeh Mahdi , Mehdizadeh Amirreza , Youssefi Navid
Published: February 2022

Objective. The aim of this study was to determine the average and maximum height of the papilla around maxillary anterior implants in respect of neighboring structures and location of implants. Materials and Methods. 92 dental implants from 63 patients were investigated in this study. Those implants were placed in the anterior maxillary region and had been loaded for a minimum of one year. After receiving written consent, clinical data including the height of interproximal papillae adjacent to the tooth/implant/pontic were obtained through clinical observation. The independent t-test or ANOVA, the regression modeling, and generalized estimating equation (GEE) models were used for statistical analysis (

p
<
0.05

). Results. Papilla height was calculated as 2.8 mm (1–5.5 mm) for implant-tooth sites, 2.6 mm (1–4 mm) in implants beside pontics, and 2.5 mm (1–3.5 mm) for implants adjacent to implants. Despite the lack of a significant difference in the mean papilla height in the studied groups, the maximum values of papilla heights were significantly different. Conclusions. In this study, no significant differences were found in papilla height mean values in relation to neighboring structures or location of implants in the anterior maxilla. However, the maximum values of papilla heights were observed around implants next to natural teeth.
Effects of the Reaction Parameters and Light Hydrothermal Aging for Catalytic Combustion of Propane over Co-Mn-Ce Catalyst
Authors: Lei Lili , Jin Miaomiao , Cui Chenrui , Li Kai , Wang Pan
Published: February 2022

A composite oxides’ Co-Mn-Ce catalyst was synthesized by a coprecipitation method, and the experiment was carried out to study the effects of reaction parameters and light hydrothermal aging on propane combustion over the Co-Mn-Ce catalyst. The influence of reaction temperature, propane concentration, oxygen concentration, water vapor, and hydrothermal aging was studied during the catalytic combustion of propane. The propane conversion significantly decreased by 10% when the propane concentration increased at 300°C and then further decreased from 80% to 40% as water vapor concentration increased from 0 to 10 vol.%. In addition, water vapor also prolonged the time required to reach equilibrium. After hydrothermal treatment, the catalyst obtained the lowest oxidation capacity of propane. Furthermore, the results of in situ DRIFTs and O2 temperature programmed desorption (O2-TPD) demonstrated that there were fewer oxygen species after hydrothermal aging, and carbonates were the main intermediates formed during the catalytic oxidation of propane.
Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment
Authors: Rakhra Manik , Sanober Sumaya , Quadri Noorulhasan Naveed , Verma Neha , Ray Samrat , Asenso Evans
Published: February 2022

Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.
[Retracted] Blockchain‐Based Deep Learning to Process IoT Data Acquisition in Cognitive Data
Authors: Hannah S. , Deepa A. J. , Chooralil Varghese S. , BrillySangeetha S. , Yuvaraj N. , Arshath Raja R. , Suresh C. , Vignesh Rahul , YasirAbdullahR  , Srihari K. , Alene Assefa
Published: February 2022

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer’s disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high‐quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain‐based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain‐based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real‐time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS‐3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.
Juvenile Crime Monitoring and Characteristic Analysis Based on the Internet of Things and Grid Management
Authors: Gong Jingyi
Published: February 2022

Over the past decade, researchers have identified intervention methods and program models that reduce juvenile delinquency and encourage prosocial development. Preventing juvenile delinquency not only saves young lives from being wasted but also prevents the onset of adult criminal careers and thus reduces the burden of crime on its victims and society. Aiming at this social problem, this study combines Internet of Things and grid models to study the attribution and prevention mechanisms of minor campus violence crimes. It realizes the modern network system mode of information management, collaborative operation, and grid integration in the target area through Internet of Things and grid models. In addition, based on consulting-related literature and practical research, the causes of campus violence crimes are analyzed through a self-organizing competitive neural network. Finally, this article combines the judicial practice of the management of campus violence and presents the prevention and control countermeasures of juvenile campus violence crimes to effectively prevent and control the occurrence of juvenile campus violence crimes.
The Prevention of Senile Diabetes by Smart Wearable Health Monitoring Products
Authors: Wei Wei , Li Fangbo
Published: February 2022

As a chronic disease, diabetes in the elderly patients is incurable and has a long treatment time, most of the symptoms are atypical, and onset is difficult to detect. This is among the challenging issues in front of doctors and paramedical stall and needed to be addressed on a priority basis as it poses a great threat to the health and life of the elderly. At present, the corresponding home medical products and emergency medical equipment cannot provide comprehensive and real-time services for monitoring physical conditions. At present, smart wearable health monitoring products are still in an emerging field. Relying on the advantages of internet technology, actively monitoring the physical condition of elderly diabetic patients is conducive to health protection. This article uses questionnaire surveys and experimental research methods to analyze the information source channels of the elderly diabetic patients interviewed for diabetes and their concerns about using smart wearable health monitoring products and compare their satisfaction with traditional blood glucose monitoring products and smart wearable health monitoring products. According to the investigation results of 50 elderly diabetes patients in P City,, they use TV, newspapers, and magazines to learn more about relevant information. They are mainly worried that they are unfamiliar with the product and will not use the process. Compared with traditional blood glucose monitoring products, smart wearable health monitoring products are satisfied, and degree is higher. Therefore, in order to better let elderly diabetic patients understand their physical condition, they can also effectively avoid the risk factors that may be suffered in life so that the disease can be effectively controlled. Various claims of the proposed scheme are verified through a rigorous evaluation process which is carried out in the form of numerous experiments. These results show that the proposed scheme is an effective way to resolve the aforementioned issue in the hospitals.
The Protective Mechanism of Dexmedetomidine on Renal in Hemorrhagic Shock
Authors: Jia Zhaojin , Chen Xiaowei , Sun Peng , Liu Mingxia , Li Xiuhua
Published: February 2022

Objective. To explore the protective effect of dexmedetomidine on renal function in patients with hemorrhagic shock and its possible mechanism. Methods. Seventy patients with traumatic hemorrhagic shock requiring surgical treatment were randomly divided into the control group (group C) and the dexmedetomidine group (group D), with 35 patients in each group. Patients in both groups were actively treated with volumetric resuscitation while surgical hemostasis. Group D was given dexmedetomidine 0.5 μg/kg before skin incision after anesthesia induction, for 10 min, followed by intravenous infusion at a rate of 0.4 μg·kg-1·h-1 until 30 min before surgery, and group C was given equal volume of normal saline before skin resection (H1). Venous blood was collected 2 h (H2) and 4 h (H4) after skin resection, and plasma levels of BUN, creatinine (SCr), lipid peroxides (MDA), and inflammatory mediators IL-6 and IL-8 were measured on the 1st and 2nd day after surgery. Results. Compared with H1, BUN and SCr levels had no significant difference at 2 h and 4 h after skin resection but significantly decreased at 1 and 2 postoperative days (D1) (

P
<
0.05

). There were significant differences in MDA, IL-6, and IL-8 at 2 and 4 h after skin resection (

P
<
0.05

), but there were no significant differences at 1 day after surgery (D1) and 2 days after surgery (D2). Compared with group C, the levels of MDA, IL-6, and IL-8 in group U were significantly decreased at 2 h and 4 h after skin resection (

P
<
0.05

), and the levels of BUN and SCr in group U were significantly decreased at 1 and 2 days after skin resection (

P
<
0.05

). Conclusion. Dexmedetomidine can effectively inhibit the release of oxygen free radicals in the shock stage and the shock recovery stage in patients with hemorrhagic trauma shock and has a protective effect on renal function.
Research on Strawberry Disease Diagnosis Based on Improved Residual Network Recognition Model
Authors: Wenchao Xu , Zhi Yan
Published: February 2022

Considering the problems of high cost, inefficiency, and time consumption of manual diagnosis of strawberry diseases, G-ResNet50 is proposed based on transfer learning and deep residual network for strawberry disease identification and classification. The G-ResNet50 is based on the ResNet50, and the focal loss function is introduced in G-ResNet50 to make the model devote itself to disease images that are difficult to classify. During the training process of the G-ResNet50 model, its convolutional layer and pooling layer inherit the pre-trained weight parameters from the ResNet50 model on the PlantVillage dataset, while adding dropout regularization and batch regularization methods to optimize the network model. The strawberry disease dataset includes four sample images of healthy plants, powdery mildew, strawberry anthracnose, and leaf spot disease. The dataset is enhanced and expanded by operations including angle rotation, adjusting contrast and brightness, and adding Gaussian noise. Compared with existing models such as VGG16, ResNet50, InceptionV3, and MobileNetV2, the results of model training and testing on 7,525 four-category leaf datasets show that the G-ResNet50 model has faster convergence speed and better classification effect, and its average recognition accuracy rate reached 98.67%, which is significantly higher than other models. Through the three evaluation indicators of precision rate, recall rate, and confusion matrix, it is concluded that the G-ResNet50 has good robustness, high stability, and high recognition accuracy and can provide a feasible solution for strawberry disease detection in practical applications.
Transformation of a Large Ancient Oil Reservoir to a Dry Gas Reservoir: A Case Study of the Kela-2 Gas Field in the Kuqa Foreland Basin, NW China
Authors: Lu Xuesong , Yu Zhichao , Liu Keyu , Zhao Mengjun , Fan Junjia , Guo Xiaowen , Zhuo Qingong , Gui Lili
Published: February 2022

Various lines of evidence, including the occurrence of bitumen and fluid inclusions, show that oil charge once took place in the Kela-2 gas field, Kuqa foreland basin, Northwest China. However, the scale of the ancient oil reservoir remains unclear, as does the process by which the reservoir evolved into the present dry gas field. Here, using data from analyses of fluid inclusions, petrography, laser Raman spectroscopy, and quantitative fluorescence, the hydrocarbon accumulation history of the Kela-2 gas field is reconstructed. The results show that the gas field underwent three periods of hydrocarbon charging and one period of adjustment. The first oil charging occurred at about 12 Ma, as recorded by the first group of oil inclusions containing 0–8 vol.% gas with yellowish-brown fluorescence. The second charging involved mature oil charging at about 4 Ma, recorded by the second group of oil inclusions containing 15–25 vol.% gas with blue-white fluorescence. According to quantitative grain fluorescence (QGF) and rock pyrolysis analysis, an ancient oil reservoir existed with an oil-column height of about 350 m, and the paleo oil–water contact was lower than the present gas–water contact. Under intense thrusting from 3 Ma, the ancient oil reservoir was destroyed, with oil escaping through the Kashangtuokai thrust fault, which broke the salt layer, as this layer at that time lay in the brittle deformation domain. The inferred destruction of the ancient oil reservoir is supported by the numerous oil and gas shows at the surface and in shallow layers near the Kashangtuokai fault, as well as the anomalous development of authigenic kaolinite in the gas reservoir, which was enhanced by an open or semiopen system caused by the fault breaking through the salt layer. Subsequently, with increasing burial depth to more than 3000 m, the fault that had cut through the salt layer annealed because the salt layer then lay within the ductile deformation domain. The higher overpressure that occurred during the third gas charging at about 2 Ma reflected the annealing of the fault in the salt layer, favoring late gas accumulation and preservation. The evolution of the Kela-2 gas field provides an important case study for understanding the role of the salt layer crossing the brittle–plastic transition and the dynamic evolution of the salt caprock in salt-containing foreland basins.
A UAV Visual Relocalization Method Using Semantic Object Features Based on Internet of Things
Authors: Wang Maolin , Wang Hongyu , Wang Zhi , Li Yumeng
Published: February 2022

Unmanned Air Vehicle (UAV) has the advantages of high autonomy and strong dynamic deployment capabilities. At the same time, with the rapid development of the Internet of Things (IoT) technology, the construction of the IoT based on UAVs can break away from the traditional single-line communication mode of UAVs and control terminals, which makes the UAVs more intelligent and flexible when performing tasks. When using UAVs to perform IoT tasks, it is necessary to track the UAVs’ position and pose at all times. When the position and pose tracking fails, relocalization is required to restore the current position and pose. Therefore, how to perform UAV relocalization accurately by using visual information has attracted much attention. However, the complex changes in light conditions in the real world have brought huge challenges to the visual relocalization of UAV. Traditional visual relocalization algorithms mostly rely on artificially designed low-level geometric features which are sensitive to light conditions. In this paper, oriented to the UAV-based IoT, a UAV visual relocalization method using semantic object features is proposed. Specifically, the method uses YOLOv3 as the object detection framework to extract the semantic information in the picture and uses the semantic information to construct a topological map as a sparse description of the environment. With prior knowledge of the map, the random walk algorithm is used on the association graphs to match the semantic features and the scenes. Finally, the EPnP algorithm is used to solve the position and pose of the UAV which will be returned to the IoT platform. Simulation results show that the method proposed in this paper can achieve robust real-time UAVs relocalization when the scene lighting conditions change dynamically and provide a guarantee for UAVs to perform IoT tasks.
PTPN6-EGFR Protein Complex: A Novel Target for Colon Cancer Metastasis
Authors: Liu Gang , Zhang Yan , Huang Yun , Yuan Xinpu , Cao Zhen , Zhao Zhanwei
Published: February 2022

This study investigates the expression of nonreceptor protein tyrosine phosphatase 6 (PTPN6) gene in different colon cancer cells and its effect on malignant biological behavior. The expression level of PTPN6 mRNA in different colon cancer cell lines was detected by qPCR. CCK-8, clone formation assay, scratch assay, and transwell assay were used to detect the effect of knockdown or overexpression of the PTPN6 gene on the malignant biological behavior of colon cancer cells. CO-IP assay was used to detect the interaction protein of PTPN6. PTPN6 was highly expressed in colorectal cancer tissues. High expression of PTPN6 is associated with poor prognosis in patients with colon cancer. PtPN6 knockdown inhibited the proliferation, invasion, migration, and clonogenesis of colorectal cancer LOVO and SW480 cells. At the same time, the knockdown of PTPN6 inhibited the EMT process in colorectal cancer. CO-IP results showed that PTPN6 had a protein-protein interaction with EGFR. Overexpression of EGFR increased the carcinogenic effect of PTPN6. The high expression of the PTPN6 gene can promote the proliferation, migration, and invasion of colon cancer cells. PTPN6 can interact with EGFR. PTPN6-EGFR complex may be an important factor affecting the biological characteristics of colon cancer cells and a potential therapeutic target.
Mitigating Hotspot Issues in Heterogeneous Wireless Sensor Networks
Authors: Khalaf Osamah Ibrahim , Romero Carlos Andrés Tavera , Hassan Shahzad , Iqbal Muhammad Taimoor
Published: February 2022

Wireless Sensor Networks (WSNs) consist of a spatially distributed set of autonomous connected sensor nodes. The deployed sensor nodes are extensively used for sensing and monitoring for environmental surveillance, military operations, transportation monitoring, and healthcare monitoring. The sensor nodes in these networks have limited resources in terms of battery, storage, and processing. In some scenarios, the sensor nodes are deployed closer to the base station and responsible to forward their own and neighbor nodes’ data towards the base station and depleted energy. This issue is called a hotspot in the network. Hotspot issues mainly appear in those locations where traffic load is more on the sensor nodes. The dynamic and unequal clustering techniques have been used and mitigate the hotspot issues. However, with few benefits, these solutions have suffered from coverage overhead, network connection issues, unbalanced energy utilization among the sink nodes, and network stability issues. In this paper, a comprehensive review of various equal clustering, unequal clustering, and hybrid clustering approaches with their clustering attributes is presented to mitigate hotspot issues in heterogeneous WSNs by using various parameters such as cluster head selection, number of clusters, zone formation, transmission, and routing parameters. This review provides a detailed platform for new researchers to explore the new and novel solutions to solve the hotspot issues in these networks.
Image Encryption Based on Hopfield Neural Network and Bidirectional Flipping
Authors: Zhang Haitao , Yang Shuangqi
Published: February 2022

Many encryption systems face two problems: the key has nothing to do with the plaintext; only a single chaotic sequence is adopted during the encryption. To solve the problems, this paper proposes an image encryption method based on Hopfield neural network and bidirectional flipping. Firstly, the plaintext image was segmented into blocks, the resulting image matrix was block scrambled, and each block was bidirectionally flipped to complete the scrambling process. After that, the plaintext image was processed by the hash algorithm to obtain the initial values and control parameters of the chaotic system, producing a pseudo-random sequence. Then, a diffusion matrix was generated through the optimization by Hopfield neural network and used to derive a ciphertext image through diffusion transformation. Experimental results show that our algorithm is highly sensitive to plaintext, strongly resistant to common attacks, and very efficient in encryption.
Association of GSTM1 and GSTT1 Null Genotypes with Toluene Diisocyanate-Induced Asthma
Authors: Lee Jong-Uk , Jeong Ji-Yeon , Kim Min Kyung , Min Sun A. , Park Jong-Sook , Park Choon-Sik
Published: February 2022

Background. Toluene diisocyanate (TDI) causes occupational asthma by generating oxidative stress, leading to tissue injury and inflammation. Glutathione transferases (GSTs) are detoxifying enzymes that eliminate oxidative stress. We examined whether the genotypes of the GSTM1 and GSTT1 genes are associated with TDI-induced occupational asthma (TDI-OA). Methods. The study population consisted of 26 asthmatics with a positive response to the TDI challenge (TDI-PA) and 27 asthmatics with negative responses (TDI-NA). GSTM1 and GSTT1 null and wild-type genotypes were determined using multiplex PCR. The plasma GSTM1 and GSTT1 protein concentrations were determined using ELISA. Results. The GSTM1 null genotype was more frequent in the TDI-PA than in the TDI-NA (77.8 vs. 50.0%, OR = 3.5,

p
=
0.03

), while the frequency of the GSTT1 null genotype tended to be higher in the TDI-PA than in the TDI-NA (59.3 vs. 42.3%, OR = 1.98,

p
=
0.21

). When analyzed together, the GSTM1/GSTT1 null genotype was more frequent in the TDI-PA than in the TDI-NA (48.2 vs. 15.3%, OR = 6.5,

p
=
0.04

). The decline in the FEV in 1 s after TDI challenge was higher with the GSTM1/GSTT1 null than the GSTM1 wild-type/GSTT1 null genotypes (24.29% vs. 7.47%,

p
=
0.02

). The plasma GSTM1 level was lower with the GSTM1 null than with the GSTM1 wild-type genotypes both before (13.7 vs. 16.6 ng/mg,

p
=
0.04

) and after (12.9 vs. 17.1 ng/mg,

p
=
0.007

) the TDI challenge, while the GSTT1 level was not changed with either the GSTT1 null or wild-type genotype. Conclusions. The GSTM1 null genotype, but not GSTT1 alone, may confer susceptibility to TDI-OA. However, the genetic effect of the GSTM1 null genotype may be enhanced synergistically by the GSTT1 null genotype. The genetic effect of GSTM1 was validated in the plasma as the GSTM1 protein level. Therefore, the GSTM1 and GSTT1 genotypes may be useful diagnostic markers for TDI-OA.