bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and Validation of a Nomogram for Predicting Mortality in Patients with Atrial Fibrillation and Acute Coronary Syndrome Who Underwent Percutaneous Coronary Intervention in a Chinese Multicenter Cohort
Authors: Hua Can , Tian Haitao , Wang Yubin , Zheng Jianyong , Liu Pengfei , Zhang Boyang , Wang Nannan , Tang Haihong , Wang Feng , Xie Xiufeng , Yuan Haifeng , Li Tianchang
Published: April 2022

Background. This study is aimed at to establish an effective prognostic nomogram for patients with atrial fibrillation (AF) and acute coronary syndrome (ACS) underwent percutaneous coronary intervention (PCI). Methods. The nomogram was based on a retrospective study of 977 patients with AF and ACS who underwent PCI who were admitted to any of the 11 tertiary hospitals in the Beijing area between 2009 and 2015. The predictive accuracy and discriminative ability of the nomogram were determined by a concordance index (

C

-index) and calibration curve and were compared using current risk scores such as GRACE, CRUSADE, CHA2DS2-VASc, and HAS-BLED. The results were validated using bootstrap resampling and a retrospective cohort study of 409 patients enrolled in Fuwai Hospital at the same institution. Results. Independent factors derived from multivariable analysis of the primary cohort to predict all-cause mortality were age, pattern of ACS, red blood cell distribution width, N-terminal proBNP, and serum creatinine, all of which were assembled into the nomogram. The calibration curve for the probability of recurrence showed that the nomogram-based predictions were in good agreement with actual observations. The

C

-index of the nomogram for predicting mortality was 0.764 (95% CI, 0.718-0.810), which was statistically higher than the

C

-index values for the current risk scores (from 0.573 to 0.681). In the validation cohort, the

C

-index of the nomogram for predicting all-cause death was 0.706 (95% CI 0.601-0.811), with no significant differences compared with GRACE and CRUSADE, but better than that of CHA2DS2-VASc and HAS-BLED. Conclusions. The nomogram has good prognostic prediction for patients with AF and ACS who underwent PCI.
Multi-region Nonuniform Brightness Correction Algorithm Based on L-Channel Gamma Transform
Authors: Qi Min , Cui Shanshan , Chang Xin , Xu Yuelei , Meng Hongying , Wang Yi , Yin Ting
Published: April 2022

Nonuniform brightness distribution of collimator reticle images leads to the recognition difficulty of reticle grids and coordinate scales on images and affects the digitalization of reticle coordinate system. The article proposes an image quality improvement algorithm that operates on L channel of LAB color space to perform brightness correction. It designs four typical image modules depending on representative brightness features. Corresponding brightness correction models for all typical image modules are constructed by designing the new correction functions based on nonlinear gamma transform. A concept of brightness similarity is defined for automatic matching between typical image modules and the image region to be processed. On this basis, the algorithm can automatically adjust brightness correction mode to ensure the brightness of each pixel tends to be uniform, which is reflected in a visible improvement in the color distortion of the image. The algorithm can effectively reduce the complexity of model processing and adaptively adjust brightness correction intensity according to the pixel brightness value. It enables more accurate corrections with good results.
Etlingera elatior Flower Aqueous Extract Protects against Oxidative Stress-Induced Nephropathy in a Rat Model of Type 2 Diabetes
Authors: Noordin Liza , Wan Ahmad Wan Amir Nizam , Muhamad Nor Nor Asiah , Abu Bakar Nor Hidayah , Ugusman Azizah
Published: April 2022

Diabetes mellitus (DM) is a known systemic disease with increasing global prevalence and multi-organ complications including diabetic nephropathy (DN). The trend of using medicinal plants in the management of DM is increasing exponentially. Etlingera elatior is a medicinal plant that contains chemicals and antioxidants that delay the oxidation process. However, available data focusing on its use on DN are inconsistent and scarce. This study aims to investigate the antidiabetic and nephroprotective effects of E. elatior flower aqueous extract (EEAE) in a type 2 DM rat (T2DR) model. The T2DR model was developed using a combination of a high-fat diet (HFD) and a low dose of streptozotocin (STZ) at 35 mg/kg. Thirty-two Sprague Dawley male rats were randomly divided into four groups (n = 8): (1) control (normal rat), (2) T2DR (untreated-type 2 diabetic rat), (3) Met (250 mg/kg metformin-treated T2DR), and (4) EEAE (1000 mg/kg EEAE-treated T2DR). All treatments were administered orally for 6 weeks. EEAE significantly reduced fasting blood glucose (FBG), microalbuminuria, serum creatinine, and serum blood urea nitrogen. EEAE also reduced malondialdehyde (MDA) and enhanced the levels of antioxidant markers—superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), and total antioxidant capacity (T-AOC). The inflammatory marker (interleukin (IL)-6) and fibrosis markers (transforming growth factor-beta (TGF-β), and connective tissue growth factor (CTGF)) were significantly decreased in the EEAE-treated group. The T2DR group developed DN, which was characterized by segmental sclerosis of the glomeruli associated with focal tubular atrophy and interstitial fibrosis. Interestingly, the histology of kidney tissue in the EEAE group was preserved. This effect was similar to that of the control drug metformin. In summary, the antidiabetic and nephroprotective effects might be related to the antioxidant properties and anti-inflammatory effects of the EEAE. The antidiabetic activity could be due to the presence of the active compound cyanidin-3-O-glycosides, which is an anthocyanin antioxidant, that is present in the EEAE. E. elatior has the potential to be developed as a natural source of antioxidants that can be used for the prevention or even the treatment of DM. These findings could lead to future research into the therapeutic use of E. elatior in alleviating the progression of DM and thus preventing nephropathy.
Medicinal Applications of Banana Peel Flour Used as a Substitute for Computing Dietary Fiber for Wheat Flour in the Biscuit Industry
Authors: Alshehry Garsa Ali
Published: April 2022

The aim of this study, medicinal applications of banana peel flour, was used to estimate nutrition value and minerals content. The results indicated that banana peel flour was safe for food application, and it was lower in protein and fat and higher in dietary fiber and total carbohydrate which were 52.68 and 67.25%, respectively. Moreover, the minerals content of phosphorous, potassium, sodium, manganese, and calcium were 217.92, 110.24, 96.43, 85.25, and 80.27 mg/100 g, respectively. Furthermore, the problem of the study investigated the effect of banana peel flour incorporation with wheat flour with 72% extraction to produce biscuit blends at different ratios of 5.0, 10.0, 15.0, and 20.0%, respectively. The nutrition value and mineral content of banana peel flour in biscuits increased gradually in the blends, according to the results. The methods of study of the four biscuit blends were prepared with banana peel flour substituted and wheat flour extraction of 72 percent, and they were compared to a control biscuit. Sensory properties reported that the control biscuit was the highest score followed by blends which had contained 5, 10, and 15% banana peel flour. Meanwhile, the blend of biscuit fortified with 20% was not accepted by panelists. From the color biscuits, the blends containing 5, 10, and 15% banana peel flour had acceptable color, and the blends' color was not acceptable. This may be caused by dark color and high dietary fiber. The texture profile analyzer and physical characteristics were confirmed and gave the same results. The result is that it could be recommended that the banana peel flour, which has high fiber content, produce acceptable biscuits fortified with 5, 10, and 15% and become functional foods. Panelists did not find the 20% blend biscuits to be particularly appealing.
Molecular Mechanisms of Coffee on Prostate Cancer Prevention
Authors: Montenegro Julia , Freitas-Silva Otniel , Teodoro Anderson Junger
Published: April 2022

Prostate cancer (PCa) is one of the most common types of cancer among men, and coffee is associated with a reduced risk of developing PCa. Therefore, we aim to review possible coffee molecular mechanisms that contribute to PCa prevention. Coffee has an important antioxidant capacity that reduces oxidative stress, leading to a reduced mutation in cells. Beyond direct antioxidant activity, coffee stimulates phase II enzymatic activity, which is related to the detoxification of reactive metabolites. The anti‐inflammatory effects of coffee reduce tissue damage related to PCa development. Coffee induces autophagy, regulates the NF‐κB pathway, and reduces the expression of iNOS and inflammatory mediators, such as TNF‐α, IL‐6, IL‐8, and CRP. Also, coffee modulates transcriptional factors and pathways. It has been shown that coffee increases testosterone and reduces sex hormone‐binding globulin, estrogen, and prostate‐specific antigen. Coffee also enhances insulin resistance and glucose metabolism. All these effects may contribute to protection against PCa development.
Study on Mechanical Behavior of Reinforced Concrete Beams under Sulfate Attack
Authors: Zang Xiaomeng , Wang Genhui , Zhang Zichen , Duan Xiaolu , Hu Xiaoying
Published: April 2022

In order to analyze the influence of sulfate corrosion on the mechanical behavior of reinforced concrete beams, the dry‐wet cyclic corrosion test was carried out on the model test beams, and the two test beams corroded for 0 d and 60 d were statically loaded, respectively. Then, the changes in flexural mechanical properties of the two test beams corroded for 0 d and 60 d were studied. Finally, the nonlinear finite element model was created by using ABAQUS. The plastic damage model is used to reflect the sulfate corrosion, and the mechanical properties of concrete beams under long‐term sulfate corrosion are further analyzed. The results show that the compressive strength of the test beam concrete after 60 days of corrosion is 13.6% higher than that before corrosion, which indicates that the strength of concrete is increased by sulfate corrosion in the early stage. With the increase of sulfate corrosion time, the strength and stiffness of the beam first increase and then decrease. With the change in corrosion time, the cracking load and flexural capacity first increase and then decrease, which is consistent with the mechanism of sulfate densification first and then deterioration of the concrete structure. The calculation formulas of cracking load and bending capacity obtained by fitting are accurate.
An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm
Authors: Xu Haiman
Published: April 2022

With the continuous development of the field of building optimization, more and more optimization methods have sprung up, among which there are many kinds of intelligent optimization algorithms. This kind of intelligent optimization algorithm usually relies on the traditional building performance simulation method to obtain the building performance index for optimization. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of building models and the configuration of computers, which leads to a long time for performance optimization, which cannot give efficient and accurate feedback to designers in engineering. Building performance optimization methods based on intelligent optimization algorithms are mainly used in scientific research and are difficult to put into practical projects. Therefore, this paper builds an accurate and efficient platform for building performance prediction and optimization to help designers make decisions combined with BP neural network and the SPEA-II multiobjective optimization algorithm. Besides, the optimization results of the case are quantitatively and qualitatively analyzed and presented in visual form based on the BP neural network prediction model. Quantitative analysis includes the evolution process of solution set, convergence process, and comprehensive quality evaluation of solution set. Qualitative analysis includes Pareto frontier and optimal architectural scheme analysis. Finally, the conclusion shows that the platform prediction and optimization can give accurate and reliable optimal solution, and the optimal building scheme is reasonable and has high engineering application value.
Fault Diagnosis in Regenerative Braking System of Hybrid Electric Vehicles by Using Semigroup of Finite-State Deterministic Fully Intuitionistic Fuzzy Automata
Authors: Kousar Sajida , Aslam Farah , Kausar Nasreen , Pamucar Dragan , Addis Gezahagne Mulat
Published: April 2022

Regenerative braking is one of the most promising and ecologically friendly solutions for improving energy efficiency and vehicle stability in electric and hybrid electric cars. This research describes a data-driven method for detecting and diagnosing issues in hybrid electric vehicle regenerative braking systems. Early fault identification can help enhance system performance and health. This study is centered on the construction of an inference system for fault diagnosis in a generalized fuzzy environment. For such an inference system, finite-state deterministic fully intuitionistic fuzzy automata (FDFIFA) are established. Semigroup of FDFIFA and its algebraic properties including substructures and structure-preserving maps are investigated. The inference system uses FDFIFA semigroups as variables, and FDFIFA semigroup homomorphisms are employed to illustrate the relationship between variables. The newly established model is then applied to diagnose the possible fault and their nature in the regenerative braking systems of hybrid electric vehicles by modeling the performance of superchargers and air coolers. The method may be used to evaluate faults in a wide range of systems, including autos and aerospace systems.
BCKDK Promotes Ovarian Cancer Proliferation and Migration by Activating the MEK/ERK Signaling Pathway
Authors: Li Huashun , Yu Dongyang , Li Lianbing , Xiao Juanjuan , Zhu Yijian , Liu Yi , Mou Li , Tian Yafei , Chen Linbo , Zhu Feng , Duan Qiuhong , Xue Peipei
Published: April 2022

Background. Ovarian cancer (OC) is the most fatal gynecologic cancer. The branched-chain α-keto acid dehydrogenase kinase (BCKDK) plays an important role in many serious human diseases, including cancers. Its function in promoting cell proliferation and migration has been reported in various cancers. However, the biological role of BCKDK and its molecular mechanisms underlying OC initiation and progression are unclear. Methods. First, the expression level of BCKDK in OC cell lines or tissues was determined using tissue microarray- (TMA-) based immunohistochemistry or western blotting. Then, growth curve analysis, anchorage-independent cell transformation assays, wound healing assays, cell migration assays, and tumor xenografts were used to test whether BCKDK could promote cell transformation or metastasis. Finally, the signaling pathways involved in this process were investigated by western blotting or immunoprecipitation. Results. We found that the expression of BCKDK was upregulated in OC tissues and the high expression of BCKDK was correlated with an advanced pathological grade in patients. The ectopic overexpression of BCKDK promoted the proliferation and migration of OC cells, and the knockdown of BCKDK with shRNAs inhibited the proliferation and migration of OC ex vivo and in vivo. Moreover, BCKDK promoted OC proliferation and migration by activating MEK. Conclusions. Our results demonstrate that BCKDK promotes OC proliferation and migration by activating the MEK/ERK signaling pathway. Targeting the BCKDK-MEK axis may provide a new therapeutic strategy for treating patients with OC.
Mathematical Modeling and Multivariable Optimization Design of SUV Chassis Structure Based on SFE CONCEPT
Authors: Liu Jingyi , Wang Suifeng , Wang Guodong
Published: April 2022

The car chassis design concept is an important part of the car design process. Analyzing the car chassis structure in the conceptual design process is of great significance for improving the efficiency and accuracy of car chassis design. This paper takes SUV models as the research object and uses SFE CONCEPT software to realize the mathematical modeling and multivariate optimization of the SUV chassis structure. The concept of “implicit” parameterization and the concept of “CAE analysis-driven design” are adopted to quickly realize large-scale geometric transformation and consistency of interconnection, while achieving automated production and grid parameters. Through this research, it can be found that in the concept development stage of the vehicle chassis structure, the CAE performance analysis is introduced into the early development stage as soon as possible; combined with the implicit parametric design method, it can quickly and effectively realize the exploration and optimization of a larger range of structures. In addition, the SFE CONCEPT software used in this article can create parametric structural models, and combined with automatic meshing technology, it can realize continuous changes of topology variables, shape variables, size variables, and material variables, and compare the design results with the detailed design results to confirm the feasibility of the design. In addition, the experimental test shows that the success rate of SFE CONCEPT and other CAD software docking reaches 100%.
Application Value of Serum Hcy, TLR4, and CRP in the Diagnosis of Cerebral Small Vessel Disease
Authors: Qu Peng , Cheng Kaili , Gao Qi , Li Yan , Wang Minghua
Published: April 2022

Objective. To evaluate the application value of combined detection of serum homocysteine (Hcy), Toll-like receptor 4 (TLR4), and C-reactive protein (CRP) in the diagnosis of cerebral small vessel disease (CSVD). Methods. 90 patients with CSVD admitted to our hospital within the past year were identified as the research subjects, and the patients with cognitive dysfunction were assigned to the experimental group, and those with normal cognitive function were assigned to the control group according to the evaluation of cognitive dysfunction by the Montreal Cognitive Assessment (MoCA), with 45 cases in each group. Results. The experimental group obtained remarkably elevated Hcy levels than the control group

P
<
0.05

. The patient’s cognitive dysfunction is mainly attributed to the impact of serum Hcy. TLR4 and Hcy were negatively correlated with MoCA scores

P
>
0.05

. In comparison with the control group, the experimental group had significantly higher levels of Hcy, serum CRP, and interleukin (IL)-6

P
<
0.05

. Conclusion. The combined detection of serum Hcy, TLR4, and CRP features a high clinical value in the diagnosis of CSVD, which contributes to the prevention and treatment of cognitive dysfunction in patients.
The Neutrophil‐Lymphocyte Ratio Is Positively Correlated with Aggression in Schizophrenia
Authors: Tong Zhu , Zhu Jing , Wang Jia-jia , Yang Yu-jing , Hu Wei
Published: April 2022

To find biomarkers to assess the risk of aggression, we looked at the association between aggression and levels of body inflammation in patients with schizophrenia. The Modified Overt Aggression Scale (MOAS) score was used to divide the aggressive (n = 72) and nonaggressive (n = 141) groups. The Brief Psychiatric Rating Scale (BPRS) is a tool for determining the severity of a patient’s condition. After measuring the number of inflammatory cells in the peripheral blood, the platelet‐lymphocyte ratio (PLR), neutrophil‐lymphocyte ratio (NLR), and monocyte‐lymphocyte ratio (MLR) were estimated. We investigated the relationship between aggressive behavior, bodily inflammation, and BPRS. Before therapy, the aggressive group’s BPRS score, white blood cell (WBC) count, neutrophil count, monocyte count, NLR, and MLR were considerably more significant than the nonaggressive group’s. After therapy, statistically significant variations in total BPRS score and neutrophil count between the two groups. According to correlation analysis before and after treatment, aggressive behavior was positively connected with neutrophil count, NLR, and BPRS score. The presence of aggressive behavior in schizophrenic patients indicates the severity of the disorder to some degree. NLR can be used as an objective biomarker to quickly assess the risk of aggression in schizophrenic patients.
A Markov Model-Based Fusion Algorithm for Distorted Electronic Technology Archives
Authors: Wang Lei
Published: April 2022

This paper presents an in-depth study and analysis of the restoration of distorted electronic technology archives using Markov models and proposes a corresponding fusion algorithm. Using the image gradient parametrization as a regular term, the filtering restoration process is constrained and the fuzzy kernel is estimated to solve the degradation problem existing in the Tibetan antiquarian literature. In the algorithmic framework of nonlocal mean filtering, the calculation of the weight function is improved to reduce the computational effort. In the simulation results, it is shown that the improved nonlocal mean filtering restoration algorithm in this paper has good overall quality evaluation performance in the restoration of text-based images. The structural similarity between the generated image and the real image is guaranteed, and the internal mixed-mode learning of a single SAR image is performed by combining the pyramidal hierarchical network structure to improve the effectiveness of the generated image in general. The method further improves the similarity between the generated and real images, while improving the accuracy of the classification based on the data expanded by the generation method. The feasibility and accuracy of the online algorithm for the parameter estimation problem of the model are illustrated through numerical experiments with two specific examples of hidden Markov models, namely, a double Gaussian mixture model and a finite-state Markov chain model with Gaussian noise. At the same time, the advantages of the algorithm proposed in this paper are demonstrated by comparing the experimental results of the online EM algorithm with those of the offline EM algorithm on a unified model. Finally, the empirical analysis is used to illustrate the application of the algorithm in practical scenarios.
Slide Attack on Full-Round ULC Lightweight Block Cipher Designed for IoT
Authors: Zhang Kai , Lai Xuejia , Wang Lei , Guan Jie , Hu Bin
Published: April 2022

To meet the security demand for IoT devices, many new lightweight block ciphers are proposed. ULC is a lightweight block cipher designed for the IoT. It has many advantages in terms of memory use, efficiency, and security. In this paper, a slide attack on full-round ULC is proposed in a related key setting. First, two properties on ULC are discovered. The first property is presented to illustrate the property of a slid pair for ULC. The second property is introduced to construct a link between some round key bits and some master key bits. Based on these properties, a key recovery attack on ULC is proposed. In this attack, with the condition of one related key, all the 80 master key bits can be recovered with the data complexity of O(232), the memory complexity of O(232), and the time complexity of O(263). The result of this paper indicates that ULC can’t resist slide attack in related key settings.
Effectiveness Evaluation Model of Students’ English Listening Ability Based on Immersive Computing
Authors: Ding Yanbin
Published: April 2022

In the context of the integration of China’s international economic development, the country has an increasing demand for high-level English professionals. Therefore, schools need to strengthen the teaching of students’ English learning and improve students’ comprehensive English ability. College English listening course is an important course to cultivate students’ English skills. Improving the teaching effect of college English listening course has become an urgent problem in English teaching. With the development of China’s network technology and education reform, a brand-new three-dimensional virtual immersive environment teaching model has emerged. This teaching model is of great help to students in learning English. This paper applies immersion theory to college English listening teaching, using virtual reality technology to create a three-dimensional virtual immersive environment for English listening teaching and using this new environment to promote English listening teaching, stimulate students’ interest in English learning, and improve students’ English. The ability of listening comprehensively improves the efficiency of English listening teaching, thereby making college English listening lessons lively and interesting and rich in information, and helps students have an immersive experience, so as to cultivate students’ interest in learning, form learning motivation, and ultimately achieve the goal of improving their English proficiency. The method proposed in this article organically integrates the teaching, learning, and evaluation of English listening. First of all, it can evaluate the attribute grasping mode and its advantages and disadvantages of groups and individuals in the process of listening and cognition. Relying on the network, it can also provide real-time networked diagnosis reports and targeted guidance. Secondly, teachers can discover the characteristics and needs of individuals and groups based on the diagnosis results, adjust and optimize their own teaching methods, and truly teach students in accordance with their aptitude. More importantly, students can find problems through self-diagnosis and obtain effective guidance and intervention, which finally help them to move towards autonomous learning. This is also the ultimate goal of modern English education. From this perspective, this research also helps to open up new directions for future English research.
The Impact of Green Organizational Identity on Green Competitive Advantage: The Role of Green Ambidexterity Innovation and Organizational Flexibility
Authors: Chen Yunqi , Gao Linlin , Zhang Yue
Published: April 2022

There is no consistent result on the relationship between green organizational identity (GOI) and corporate green competitive advantage (GCA) in the existing literature. The omission of crucial mediating variables may be one of the important reasons. Based on the organizational identity theory and exploration-exploitation paradigm, this article studies the relationship between GOI and GCA in a model that includes mediating variables composed of green exploitative innovation (GEI) and green exploratory innovation (GER), and organizational flexibility (OF) as a moderating variable. The empirical test of survey data from polluting enterprises in China shows that GOI has a positive effect on green ambidexterity innovation and GCA. Both GEI and GER have a positive impact on GCA. Moreover, green ambidexterity innovation has a partial mediating effect between GOI and GCA, but the moderating effect of OF between green ambidexterity innovation (GAI) and GCA is different, and OF only negatively moderates the relationship between GER and GCA. These findings suggest that GOI can influence firms to choose different innovative ways between GEI and GER to obtain GCA, which counts on different levels of OF.
Software Vulnerability Mining Techniques Based on Data Fusion and Reverse Engineering
Authors: Liu Tieming
Published: April 2022

Software vulnerability mining is an important component of network attack and defense technology. To address the problems of high leakage rate and false positive rate of existing static analysis methods, this paper proposes a static analysis vulnerability detection technique based on data fusion for source code. By parsing the analysis results of different detection methods and fusing the data, the technique can effectively reduce the false positive rate and the false positive rate. A prototype of a scalable source code static analysis tool is designed and implemented, which can be automatically optimized by user feedback. Finally, an example is given to demonstrate how to uncover buffer overflow software vulnerabilities in the helpctr program based on reverse engineering techniques. The experimental results show that the false positive and false negative rates are significantly reduced compared to individual vulnerability detection methods.
SVM Classification Method of Waxy Corn Seeds with Different Vitality Levels Based on Hyperspectral Imaging
Authors: Wang Jinghua , Yan Lei , Wang Fan , Qi Shanshan
Published: April 2022

The vitality of corn seeds is a significant indicator for assessing the quality and yield of crops. In recent years, numerous information technologies have been adopted to analyze the seed vitality and provide support for efficient equipment. However, there are still some shortcomings in these technologies, which decrease the accuracy of identifying the seed vitality for various practical applications. In this paper, a synthesized classification method for seed vitality was proposed based on multisensor hyperspectral imaging. Firstly, hyperspectral images in the range of 370-1042 nm were collected for waxy corn seeds, which were subjected to aging processing with four periods of time (0, 3, 6, and 9 d). Besides, some preprocessing techniques including standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, and first-order and second-order derivatives were employed to suppress noise interference in raw spectra. In addition, principal component analysis (PCA), 2nd derivatization, and successive projection algorithm (SPA) were adopted to select feature wavelengths. Moreover, SVM classification models based on full spectra and feature wavelengths were established. The results showed that, based on feature wavelengths selected by SPA, the SVM model preprocessed by multiplicative scatter correction (MSC) had the optimal performance. The training accuracy and testing accuracy of this model were 100% and 97.9167%, respectively. RMSE was 0.018 and

R

2

was 0.875. Therefore, it can be demonstrated that the pattern recognition algorithm could achieve a high accuracy in classifying accelerated aging seeds. This algorithm provides a new method for machine learning (ML) in nondestructive detection of crops.
Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes
Authors: Kim Chansoo , Cho Sungjin , Sunwoo Myoungho , Resende Paulo , Bradaï Benazouz , Jo Kichun
Published: April 2022

LiDAR-based localization has been widely used for the pose estimation of autonomous vehicles. Since the localization requires a sustainable map reflecting environment changes, a map update framework based on crowd-sourcing measurements has been researched. Unfortunately, a point cloud map occupies too large data size to transmit data in the uploading and downloading of the map update framework. To realize the LiDAR map update framework by reducing the data size, we proposed a novel map update framework using a Geodetic Normal Distribution (GND) map that compresses the point cloud to the normal distributions. The proposed GND map update framework comprises two parts: map change detection based on crowd-sourcing vehicles and map updating based on a map cloud server. GND map changes are detected based on an evidence theory considering geometric relationships between the GND map and crowd-sourcing measurements and uploaded to the map cloud server. Uploaded map changes reproduce representative map changes based on a similarity-based clustering, which are updated into the GND map. The proposed framework was evaluated in simulations and real environments on construction sites. As a result, although partial map changes occurred, the GND map was kept up-to-date through the proposed framework and the localization for autonomous driving was performed successfully.
Mean Estimation of a Sensitive Variable under Nonresponse Using Three-Stage RRT Model in Stratified Two-Phase Sampling
Authors: Onyango Ronald , Oduor Brian , Odundo Francis
Published: April 2022

The present study addresses the problems of mean estimation and nonresponse under the three-stage RRT model. Auxiliary information on an attribute and variable is used to propose a generalized class of exponential ratio-type estimators. Expressions for the bias, mean squared error, and minimum mean squared error for the proposed estimator are derived up to the first degree of approximation. The efficiency of the proposed estimator is studied theoretically and numerically using two real datasets. From the numerical analysis, the proposed generalized class of exponential ratio-type estimators outperforms ordinary mean estimators, usual ratio estimators, and exponential ratio-type estimators. Furthermore, the efficiencies of the mean estimators are observed to decrease with an increase in the sensitivity level of the survey question. As the inverse sampling rate and nonresponse rate go up, so does the efficiency of the mean estimators, which makes them more accurate.
Packaging Big Data Visualization Based on Computational Intelligence Information Design
Authors: Zhang Guangchao
Published: April 2022

A method based on a computational intelligence information model is proposed to study the visualization of large data packages. Since the CAIM algorithm only considers the distribution of the largest number of classes in an interval, it offers an optimization method and simultaneously determines the appropriate stopping conditions to avoid overcrowding. The effectiveness of the improved algorithm has been experimentally proven. Methods of character reduction and weight determination are used to reduce the index and weight, establishing a large packaging information system. Experimental results show that the improved algorithm in this article produces more classification rules than the CAIM algorithm, because the discrete intervals created by the CAIM algorithm are relatively simple, but the classification rules are few, but less than the number of CAIM algorithms. Classification rules are generated by entropy-based sampling algorithms. This can make the classification rules simple and universal, and it is clear that the optimal sampling algorithm is more accurate than the CAIM algorithm.
User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis
Authors: Achyutha Prasad N. , Chaudhury Sushovan , Bose Subhas Chandra , Kler Rajnish , Surve Jyoti , Kaliyaperumal Karthikeyan
Published: April 2022

The stock market prices of the company vary in a daily fashion. The social media pattern usage of the company can be determined to find the sentiment score values. The dependency factor between the social media tweet platform and the performance of an organization can have how much effect on the stock prices is determined. The historical data from the Yahoo Finance APIs are taken for the unique company ID and then the probability of stock being good or bad is determined. Also, the tweets related to the company are scanned and analyzed to find the positive and negative scores. The concentration value connected to growth, the intensity of capital expenditure, and the volume of promotion were among the factors utilized in the stock’s modeling. This paper also takes the yearly finances of the end-user based on LIC payments, medical insurance payments, and average rent and then performs a classification of the user. Based on the user classification, companies are recommended to the end-user based on descending order of stock value. The average volume, average price, average market index, average daily turnover, and sentiment discrepancy index are based on the tweets of a company and the predicted value of its performance. For the classification of the user, we make use of the support vector machine algorithm. For the sentiment analysis of the tweets, the naïve Bayes algorithm is made use of, and then stock classification is done based on mathematical modeling, which includes the sentiment analysis index.
Identification of Insider Trading in the Securities Market Based on Multi-task Deep Neural Network
Authors: Li Guofeng , Li Zuojuan , Wang Zheji , Zhang Ke
Published: April 2022

Illegal insider trading identification is of great significance to the healthy development of the securities market. However, with the development of information technology, problems such as multidata sources and noise bring challenges to the insider trading identification work. Moreover, most of the current research on insider trading identification is based on single-task learning, which treats enterprises in different industries as a whole. This may ignore the differences between insider trading identification in different industries. In this article, we collect indicators from multiple sources to help regulators identify insider trading and then use information gain and correlation analysis to screen the indicators. Finally, we propose a multitask deep neural network with insider trading identification in different industries as different subtasks. The proposed model takes into account the correlations and differences between different tasks. Results of experiments show that compared with logistic, support vector machine, deep neural network, random forest, and extreme gradient boosting model, the proposed model can identify insider trading of enterprises in different industries more accurately and efficiently. This article provides new ideas for market regulators to maintain the order of the securities market through intelligent means.
PATZ1 Induces Apoptosis through PUMA in Glioblastoma
Authors: Tao Xiang , Zhang Ge , Liu Junhui , Ji Baowei , Xu Haitao , Chen Zhibiao
Published: April 2022

Aim. This study was aimed at investigating the mechanism of PATZ1 inducing apoptosis through PUMA in glioblastoma. Overexpressed PATZ1 was transfected to explore its role in inducing apoptosis in glioblastoma cells. Methods. The expression of protein was detected by western blotting assay. qRT-PCR assay was used to detect the expression of RNA. Confocal microscopy was used to analyze the correlation between PATZ1 and PUMA. TUNEL assay was used to detect the cell apoptosis. The ability of cell proliferation was detected by MTT assay and EDU assay. The effects of PATZ1 on cell apoptosis and tumor proliferation were observed in vivo by tumor xenograft mouse model. Results. The results showed that low PATZ1 expression correlates with poor prognosis in glioblastoma patients. Overexpression of PATZ1 inhibits glioma cell proliferation and induces apoptosis by activating intrinsic apoptotic pathways. PATZ1 colocalizes intracellularly with PUMA inducing apoptosis through PUMA in glioblastoma. Conclusion. PATZ1 plays a biological regulatory role in inducing apoptosis in glioblastoma, and this regulatory effect is related to PUMA, and the specific mechanism remains to be further explored.
Rail Multi Vehicle Scheduling Method for Intermediate Depot of Steel Plant Based on Improved Genetic Algorithm
Authors: Fang Bo , Feng Ming , Qiu Cheng , Zhang Ximing
Published: April 2022

Appropriate transportation network plays a significant role in the economic development of country. The rising transport demand increases the congestion in railway networks, and thus, they become more interdependent and more complex to operate. A ground railway system is established in the reservoir area of steel plants to ensure smooth running of multi vehicle logistics transportation. In recent years, the development of the ground rail multi vehicle logistics system in the reservoir area of steel plants has been increased. However, such logistic systems need an intelligent dispatching control system to avoid the possibility of safety hazards, to follow optimal tracks and to effectively manage logistics operation. In this paper, an improved hybrid genetic algorithm is proposed to realize the decision making and control of multi vehicle scheduling. Task assignment to multiple vehicles and multi-stage subparent vehicle scheduling is performed based on the information obtained from the concerned subsection. The self-learning hybrid algorithm works on the data extracted from an improved population and suggests an optimized solution. The individual behavior and optimization process are updated by self-learning, which ensures the effectiveness of iterative evolution. The selection, cross mutation, and self-learning expert base operation methods of the hybrid genetic algorithm are optimized. The proposed system is evaluated by taking the multi vehicle logistics system of cold rolling intermediate steel depot as a case study. The improved algorithm is implemented in MATLAB and is compared with the traditional genetic and particle swarm optimization algorithms. Results of the analysis prove that the hybrid genetic algorithm of self-learning knowledge expert base is effective in solving the multi vehicle logistics scheduling optimization problem. With the inclusion of the self-learning knowledge expert base genetic algorithm, the convergence trend of the proposed algorithm is enhanced; the traditional genetic algorithm converges after 140 iterations, while in the proposed algorithm, it is reduced to 100 iterations. The evaluation reveals that the proposed algorithm is speedier than the traditional genetic algorithm (GA) and particle swam optimization (PSO); the average solution time of the proposed algorithm is 119.1 while that of GA, PSO is 137.4, 131.4, respectively. The proposed algorithm is applicable to improve the operation and efficiency of the logistics transportation system. The approach is beneficial in intelligent control of metallurgical production. The proposed algorithm has practical significance to be followed in the development of intelligent metallurgical production and logistics transportation.
Treatment of Idiopathic Membranous Nephropathy for Moderate or Severe Proteinuria: A Systematic Review and Network Meta-Analysis
Authors: Chen Miaomiao , Liu Jiarong , Xiong Yi , Xu Gaosi
Published: April 2022

Objective. Numerous studies have demonstrated that the efficacy of drugs differs in idiopathic membranous nephropathy (IMN) patients with moderate or high proteinuria. However, there is no systematic comparison confirming it. This network meta-analysis (NMA) was performed to respectively compare the efficacy of ten IMN treatments in patients with moderate and high proteinuria and compare the risk of adverse events with 10 IMN regimens. Methods. Randomized controlled trials (RCTs) and observational studies analyzing the main therapeutic regimens for IMN were included from some databases. Network comparisons were performed to analyze the rates of total remission (TR), bone marrow suppression, and gastrointestinal symptoms. The surface under the cumulative ranking area (SUCRA) was calculated to rank interventions. Results. Seventeen RCTs and eight observational studies involving 1778 patients were pooled for comparison of ten interventions. Steroid + tacrolimus (TAC) showed the highest probabilities of TR whether patients had severe proteinuria or not (SUCRA 89.5% and 88.9%, separately). Rituximab (RTX) was more beneficial for TR on patients with proteinuria <8 g/d (SUCRA 66.0%) and was associated with a lower risk of bone marrow suppression and gastrointestinal symptoms (SUCRA 21.7% and 21.4%, separately). TAC + RTX and steroids + cyclophosphamide induced the highest rates of bone marrow suppression (SUCRA 90.6% and 88.3%, separately) and gastrointestinal symptoms (SUCRA 86.0% and 72.1%, separately). Conclusions. Steroids + TAC showed significant efficacy in patients with all degrees of proteinuria, while RTX was more effective in patients with moderate proteinuria and was safer in bone marrow suppression and gastrointestinal symptoms.
Prediction Algorithm of Digital Economy Development Trend Based on Big Data
Authors: Xiangyan Pan
Published: April 2022

With the development of economic globalization, people have higher requirements for economic analysis and prediction, while the prediction accuracy of the traditional economic analysis and prediction model is quite low. Therefore, with the adaptation of the times, the “big data and artificial intelligence” technology is presented for researchers and data analysts. Based on the “big data and artificial intelligence” technology, economic analysis and prediction have unique advantages in many aspects. This study analyses the impact of “big data and artificial intelligence” technology on economic analysis and prediction. Based on the mean square error criterion, the variable selection method for constructing the interval data model is given. The interval financial time-series data of stock market, fund market, futures market, and money market are used to predict and analyze the macroeconomy, and the macroeconomic interval prediction method is given, which is different from the traditional point data model. The empirical results show that the Shenzhen Component Index, Shanghai fund index, futures market transaction amount, and narrow money supply in the interval financial data have little fitting error to the macroeconomic interval prediction model. Through the first mock examination, we get the macroeconomic forecasting model based on interval financial time-series data. We also use the single model structure and the combined model structure to give the macroeconomic change interval of 2020–2023 years in China. The experimental research results show that the proposed model has good performance in the prediction of economic development trends and that it can be used for forecasting subsequent economic development forecasts.
Quantitative Risk Assessment of a Large-Scale Central Spherical Detector System by an Improved Fuzzy Fault Tree Analysis
Authors: Tan Runze , Li Xiaobin , Yue Jingxia , Du Zhipeng
Published: April 2022

Central spherical detector is one of the most essential and complicated support systems for the large-scale underground neutrino observatory. If a central spherical detector accident happens, the consequences may be disastrous with huge economic loss. However, there is very few published research works which focus on its quantitative risk analysis. In this paper, an improved fuzzy fault tree analysis (FFTA) method incorporated with the weakest t-norm (Tω) algorithm, the confidence level concept, and the analytic hierarchy process (AHP) approach is proposed to perform its risk assessment. By carrying out the identification of failure modes and failure reasons, fault tree (FT) model of central detector is constructed based on logical relationship among subcomponents. Fuzzy set theory is applied to obtain failure data, and Tω algorithm is exploited to eliminate fuzzy accumulation in the aggregation process. In addition, a confidence level coefficient and AHP approach are employed to enhance the reliability of the evaluation. Both importance and sensitivity analysis have been conducted to identify the critical basic events and provide improvement measures. Finally, the comparison of the occurrence possibility of detector failure is used to verify the applicability and the feasibility of proposed method. The calculated results indicate that the improved approach is more consistent with real situation and can provide a more effective engineering reference for the risk decision of central spherical detector.