bluuebunny/crossref_metadata_2025 huggingface dataset:
Local Binary Patterns Based on Neighbor-Center Difference Image for Color Texture Classification with Machine Learning Techniques
Authors: Verma Himangi , Vidyarthi Aditya , Chitre Abhijit V. , Wanjale Kirti H. , Anusha M. , Majrashi Ali , Hinga Simon Karanja
Published: August 2022

This is a topic that receives a lot of interest since many applications of computer vision focus on the detection of objects in visually appealing environments. Information about an object’s appearance and information regarding the object’s motion are both used as crucial signals in the process of identifying and recognising any given item. This information is used to characterise and recognise the item. The identification of objects based solely on their outward appearance has been the subject of a substantial amount of research. However, motion information in the recognition task has received only a marginal amount of attention, despite the fact that motion plays an essential role in the process of recognition. In order to analyze a moving picture in a way that is both fast and accurate, it is required to make use of motion information in conjunction with surface appearance in a strategy that has been designed. Dynamic texture is a kind of visual phenomenon that may be characterised as a type of visual phenomenon that shows spatially repeated features as well as some stationary properties during the course of time by using methodologies that are associated with machine learning. The design of modern VLSI systems takes into consideration a larger chip density, which results in a processor architecture with several cores that are capable of performing a wide range of functions (multicore processor architecture). It is becoming more challenging to run such complicated systems without the use of electric power. In order to increase the effectiveness of power optimization strategies while maintaining system performance for text data extraction, it has been developed and put into practice power optimization strategies that are based on scheduling algorithms. Over the last twenty years, texture analysis has been an increasingly busy and profitable field of study. Today, texture interpretation plays a vital role in various activities ranging from remote sensing to medical picture analysis. The absence of tools to newline analyze the many properties of texture pictures was the primary challenge faced by the texture analysis approach. Texture analysis may be roughly categorised as texture classification, texture segmentation, texture synthesis, and texture synthesis. Texture categorization is useful in numerous applications, such as the retrieval of picture databases, industrial agriculture applications, and biomedical applications. Texture categorization relies on three distinct methods, namely, statistical, spectral, and structural methods. Statistical methods are based on the statistical characteristics of the image’s grey level. Features are collected using second order statistical order, autocorrelation function, and grey level co-occurrence matrix function.
The BioFire® RP2.1 Panel Did Not Identify Concurrent Respiratory Virus Infection in Adults with Variable SARS-CoV-2 Disease Severity and Infection Duration
Authors: Quicke Kendra M. , Baxter Bridget A. , Stromberg Sophia , Gallichotte Emily N. , Fitzmeyer Emily , Young Michael C. , Pabilonia Kristy L. , Ehrhart Nicole , Dunn Julie , Ebel Gregory D. , Ryan Elizabeth P.
Published: August 2022

SARS-CoV-2 emerged in 2019 and rapidly surged into a global pandemic. The rates of concurrent infection with other respiratory pathogens and the effects of possible coinfections on the severity of COVID-19 cases and the length of viral infection are not well defined. In this retrospective study, nasopharyngeal swab samples collected in Colorado between March 2020 and May 2021 from SARS-CoV-2 PCR-positive individuals were tested for a panel of 21 additional respiratory pathogens, including 17 viral and 4 bacterial pathogens. We detected significant positive correlations between levels of SARS-CoV-2 RNA and infectious virus titers for both cohorts, as well as a positive correlation between viral RNA levels and disease severity scores for one cohort. We hypothesized that severe COVID-19 cases and longer SARS-CoV-2 infections may be associated with concurrent respiratory infections. Only one individual exhibited evidence of a concurrent infection- SARS -CoV-2 and human rhinovirus/enterovirus- leading us to conclude that viral respiratory coinfections were uncommon during this time and thus not responsible for the variations in disease severity and infection duration observed in the two cohorts examined. Mask wearing and other public health measures were imposed in Colorado during the time of collection and likely contributed to low rates of coinfection.
Some Fractional Integral Inequalities Involving Mittag‐Kernels
Authors: Zhang Xiujun , Farid Ghulam , Reunsumrit Jiraporn , Ahmad Ayyaz , Sitthiwirattham Thanin
Published: August 2022

This paper aims to present fractional versions of Minkowski‐type integral inequalities via integral operators involving Mittag‐Leffler functions in their kernels. Inequalities for various kinds of well‐known integral operators can be deduced by selecting specific values of involved parameters. Some particular cases of main results provide connection with the inequalities which have been published in recent years.
Recent Techniques in Determining the Effects of Climate Change on Depressive Patients: A Systematic Review
Authors: Ab Kader Nur Izzati , Yusof Umi Kalsom , Khalid Mohd Nor Akmal , Nik Husain Nik Rosmawati
Published: August 2022

Climate change is amongst the most serious issues nowadays. Climate change has become a concern for the scientific community as it could affect human health. Researchers have found that climate change potentially impacts human mental health, especially among depressive patients. However, the relationship is still unclear and needs further investigation. The purpose of this systematic review is to systematically evaluate the evidence of the association between climate change effects on depressive patients, investigate the effects of environmental exposure related to climate change on mental health outcomes for depressive patients, analyze the current technique used to determine the relationship, and provide the guidance for future research. Articles were identified by searching specified keywords in six electronic databases (Google Scholar, PubMed, Scopus, Springer, ScienceDirect, and IEEE Digital Library) from 2012 until 2021. Initially, 1823 articles were assessed based on inclusion criteria. After being analyzed, only 15 studies fit the eligibility criteria. The result from included studies showed that there appears to be strong evidence of the association of environmental exposure related to climate change in depressive patients. Temperature and air pollution are consistently associated with increased hospital admission of depressive patients; age and gender became the most frequently considered vulnerability factors. However, the current evidence is limited, and the output finding between each study is still varied and does not achieve a reasonable and mature conclusion regarding the relationship between the variables. Therefore, more evidence is needed in this domain study. Some variables might have complex patterns, and hard to identify the relationship. Thus, technique used to analyze the relationship should be strengthened to identify the relevant relationship.
Application of Optimized BP Neural Network In Financial Alert System
Authors: Gao Qiaoyi
Published: August 2022

The development of economic globalization not only brings development opportunities for enterprise development but also causes the rapid changes of economic environment. Global enterprises are facing many development challenges. It is very significant to implement financial alert. However, most of the existing financial alert systems are based on traditional quantitative analysis and cannot face the demand of big data growth. Based on this, main body of a book studies and analyzes the request of optimized BP neural network in financial alert system. Based on the financial early warning and a brief analysis of the development requirements of BP neural network, this paper establishes a financial early warning model for BP neural network. Considering the shortcomings of BP neural network, genetic algorithm is used to optimize the specification. Finally, a new reference scheme is provided for economic early warning. And use the following steps to create a list, taking into account the volume analysis in the conceptual design. The emulation results indicates that the optimized BP neural network can accelerate the convergence, has strong stability, and has higher accuracy in financial alert.
DDTC Suppresses Ovarian Cancer Development via the PI3K/AKT/mTOR Signaling Pathway
Authors: Li Meng , Zhang Wenqi , Wang Yihan , Huang Kai , Sun Tao , Qiu Zhicong , Yang Linqi , Wu Meng , Zhang Xiaolu , Zhang Wei
Published: August 2022

In prior research, 6,12-diphenyl-3,9-diazatetraasterane-1, 5, 7, 11-tetracarboxylate (DDTC) has been shown to be an effective inhibitor of the growth of the SKOV3 and A2780 ovarian cancer (OC) cell lines. Flow cytometry analyses indicated that DDTC was able to suppress P-CNA expression at the protein level within OC cells, while RNA-seq indicated that DDTC treatment was associated with marked changes in gene expression profiles within A2780 cells. Molecular docking analyses suggested that DDTC has the potential to readily dock with key signaling proteins including PI3K, AKT, and mTOR. In line with these findings, DDTC treatment inhibited the growth of xenograft tumors in a mouse model system. Such treatment was also associated with reduced p-PI3K/PI3K, p-AKT/AKT, p-mTOR/mTOR, and CyclinD1 (CCND1) expressions and with the increased expression of PTEN in vitro and in vivo. Together, these results suggest that DDTC is capable of readily inhibiting OC development at least in part via targeting and modulating signaling via the PI3K/AKT/mTOR axis.
Design of Digital Media Advertisement from the Perspective of Base Image Schema Based on Web
Authors: Lei Yue
Published: August 2022

With the development of improved augmented reality (AR) devices, the use of AR in new media to better communicate culture has become a major trend. However, AR technology algorithms still have many shortcomings, and this paper proposes a new algorithm that shows that the proposed algorithm is better than traditional terrain generation algorithms in terms of display and granularity and less costly than traditional geographic algorithms, according to the proposed algorithm, traditional genealogy algorithms and terrain sequencing algorithms. Therefore, the proposed area adjustment algorithm in this paper can improve the lower cost of equipment to reflect the impact of the geographic information scene function in the attachment, thus optimising the use of AR technology in new media, and the combination of the two allows the dissemination of traditional culture to be enhanced in many ways.
Application of Road Extraction from High-Resolution Remote Sensing Images in Tourism Navigation and GIS
Authors: Wang Guixia , Ma Chengguo , Liang Xin
Published: August 2022

In order to quickly and accurately obtain and process road information and its damage information, to obtain better tourism navigation experience and accurate geographic information, this paper proposes a road extraction method based on high-resolution remote sensing images. Based on the known road width information and the analysis of the gray-scale characteristics of the road surface, the method takes the road center line and straight line in the image as the center line and then deduces the road edge line information, so as to extract the edge information. Then, through the image de redundancy information processing, the final obtained road width parameters are compared to verify the accuracy and reliability of the method. The experiment shows that the road information can be extracted accurately, and the extraction accuracy and efficiency have been significantly improved, reaching more than 90%, and can directly reflect the road edge line, median line, and width information. Conclusion. The road extraction method based on high-resolution remote sensing images can effectively and accurately extract road information, obtain better tourism navigation experience, and accurate geographic information.
Shenhuang Plaster Application Improves Gastrointestinal Motility in Mice with Postoperative Ileus through Intestinal Microbiota
Authors: Shi Yanan , Xu Xiao , Liu Ting , Wang Rongyun , Xu Jingming , Wu Yujing , Ding Bin , Sun Qiuhua
Published: August 2022

Postoperative ileus (POI) is a common surgical complication, and its incidence remains high. Shenhuang Plaster (SHP) is a famous traditional Chinese medicine with a definite curative effect on postoperative intestinal dysfunction; however, the mechanisms involved in these effects are unclear. Accordingly, in this study, we constructed a POI mouse model and used the intestinal flora as the target to explore the regulatory effect of SHP on gastrointestinal motility. The results illustrated that SHP applied at the Shenque acupoint promoted the recovery of gastrointestinal motility, relieved intestinal villus atrophy and basal damage caused by POI, protected the integrity of intestinal tissue morphology, and alleviated the inflammatory response in the intestinal tissue of POI model mice. In addition, we clarified the role of the intestinal flora in the occurrence and development of POI, further evaluated the changes in the intestinal flora in each group of mice, and analysed the regulatory effect of SHP on the intestinal flora in mice with POI. The results suggested that SHP might improve gastrointestinal motility disorder in POI mice by effectively regulating intestinal flora.
Correlation between Retinol Binding Protein 4 and CT Perfusion Imaging and Prognosis in Patients with Acute Ischemic Stroke Based on Cell Imaging Data Analysis
Authors: Liu Chao , Gu Juxian , Yao Yan , Guo Ping , Hu Hongwei
Published: August 2022

Objective. In order to explore the correlation between the retinol binding protein 4 (RBP4) and prognosis of patients with acute ischemic stroke (AIS), CT perfusion imaging can be used to scan the brain tissue of patients, which can identify abnormal perfusion areas and ischemic penumbra brain tissue, so as to provide a basis for doctors to formulate a reasonable clinical treatment plan. Methods. 200 patients with first‐episode acute ischemic stroke were selected from the Department of Neurology of our hospital, including 128 males and 72 females, aged between 32 and 85 years, with an average age of 45 ± 4.3 years. After admission, the patients were tested for xanthol binding protein 4 in time, the patient’s demographic data and the basic clinical data were recorded, the degree of brain injury was evaluated, and the short‐term outcome was evaluated after treatment. Results. A total of 200 AIS patients with different degrees of brain injury were included in this study, including 128 males and 78 females, aged between 32 and 85 years, with an average age of 45 ± 4.3 years. Among them, 100 patients used CT perfusion imaging for brain scanning as the observation group, 100 patients used traditional imaging methods as the reference group, and 100 healthy people were included as the blank group. At the same time, the contents of total cholesterol, triacylglycerol, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol were measured by using an automatic biochemical analyzer. The patients were evaluated in the early stage of treatment and the effect of prognostic intervention was recorded. Conclusion. The application of CT perfusion imaging in the adjuvant treatment of AIS patients was helpful to identify the abnormal perfusion area and the brain tissue of ischemic penumbra, so as to provide a basis for doctors’ follow‐up treatment. At the same time, AIS patients with high serum RBP4 level had mild stroke severity, good short‐term prognosis, and improved treatment effect, which improved patients’ quality of life.
Study on Effects of Cyclophosphamide Combined with Vinorelbine in Advanced Small Cell Lung Cancer and Anteroposterior Changes in MRI
Authors: Li Zhichun , Ren Liliang
Published: August 2022

Objective. To explore the effects of cyclophosphamide combined with vinorelbine in advanced small cell lung cancer (SCLC) and anteroposterior changes in MRI. Methods. The clinical data of 90 patients with advanced SCLC admitted to our hospital from April 2020 to April 2021 were retrospectively analyzed. They were divided into the control group and the study group according to the order of admission, with 45 cases in each group. The control group received the routine treatment, while the study group was treated with cyclophosphamide and vinorelbine to compare the indexes of imaging data and clinical indicators between the two groups before and after treatment. Results. There was no significant difference in the indexes of imaging data between the two groups before treatment (P > 0.05), and the indexes of imaging data in the study group were visibly lower than those in the control group after treatment (P < 0.001). The DCR in the study group was significantly higher than that in the control group after treatment (P < 0.05), while the QLQ‐C30 scores and serum indices of the study group after treatment were significantly lower than those of the control group (P < 0.001). Conclusion. Patients with advanced SCLC were treated with cyclophosphamide and vinorelbine, which can effectively improve the quality of life and reduce the expression of inflammatory factors. This treatment model has a higher application value, and the treatment value is also reflected compared with the routine treatment. At the same time, the permeability parameters obtained by MRI can predict the therapeutic effects of cyclophosphamide and vinorelbine, and further studies are helpful to establish a better solution for patients.
Eigenpairs for the Analysis of Complete Lyapunov Functions
Authors: Argáez Carlos , Giesl Peter , Hafstein Sigurdur Freyr
Published: August 2022

A complete Lyapunov function describes the qualitative behaviour of a dynamical system: the areas where the orbital derivative vanishes and where it is strictly negative characterise the chain recurrent set and the gradient‐like flow, respectively. Moreover, its local maxima and minima show the stability properties of the connected components of the chain recurrent set. In this study, we use collocation with radial basis functions to numerically compute approximations to complete Lyapunov functions and then localise and analyse the stability properties of the connected components of the chain recurrent set using its gradient and Hessian. In particular, we improve the estimation of the chain recurrent set, and we determine the dimension and the stability properties of its connected components.
Clinical Effect of Standardized Dietary Avoidance Therapy on Children with Milk Protein Allergy and Its Effect on Intestinal Flora
Authors: Hao Wenying , Zhu Chun , Chen Yibo , Li Haibo , Zhu Shufang , Wang Xiaojun
Published: August 2022

Objective. The aim of this study is to analyze the clinical effect of standardized dietary avoidance therapy on children with cow milk protein allergy (CMPA) and its effect on the intestinal flora. Methods. The clinical data of 200 children with CMPA from our hospital from February 2020 to May 2021 were collected, and they were divided into a study group (n = 100) and a routine group (n = 100) based on different intervention modalities. The routine group received routine treatment, whereas the standardized dietary avoidance therapy was used in the study group. The clinical effects and related intestinal microflora indexes of the two groups were analyzed and compared. Results. There was no significant difference in the incidence of related symptoms between the two groups before intervention (

P
>
0.05

), and the conditions of the two groups were improved after intervention. The incidences of skin (2%), digestive tract (3.00%), and respiratory tract (1.00%) in the study group were significantly lower than those in the routine group (14.00%, 18.00%, and 11.00%) (

P
>
0.05

). The time taken for complete remission of symptoms and milk tolerance months in the study group (41.23 ± 23.68, 13.28 ± 6.17) were significantly shorter than those in the routine group (145.14 ± 66.74, 16.17 ± 8.05) (

P
>
0.05

). The values of height, weight, and head circumference (HC) of children in the study group (79.88 ± 2.18, 11.09 ± 1.34, 47.88 ± 0.63) were higher than those in the routine group (76.21 ± 2.34, 9.81 ± 1.18, 45.98 ± 0.59) (

P
>
0.05

). The levels of Lactobacillus and Enterococcus (9.95 ± 0.89, 11.31 ± 1.05) in the study group were higher than those in the routine group (9.11 ± 0.74, 10.38 ± 0.94), and the levels of yeast-like fungi in the study group (3.08 ± 0.24) were lower than those of the routine group (3.82 ± 0.31) (

P
>
0.05

). Conclusion. The standardized dietary avoidance therapy is remarkable in the treatment of CMPA, in which the children were able to tolerate ordinary milk earlier, and the intestinal flora was significantly improved, thereby promoting the growth and development of children. It therefore merits clinical promotion.
Application of Personalized Demand Based on Kano Model in Private Colleges
Authors: Qiu Youmei
Published: August 2022

In recent years, with the continuous growth of college students’ personalities, the development of their personalized education needs has become more and more prominent, and how to meet those needs has become the focus of current research. From the perspective of Kano, this paper establishes a “student‐centered” learning situation analysis system, based on the degree of support for graduation requirements and the training objectives for the individual needs of students. Furthermore, the paper builds a trinity of individual needs‐oriented education evaluation mode, so as to realize the basic needs, expected needs, and attractive needs of students, and forms a teaching implementation plan for personalized needs from the perspective of Kano. Then, the established teaching implementation plan of individual needs from the perspective of Kano is applied to the “applied statistics” course of economic management students in a private college, and the experimental results of the undergraduate students and the students who are promoted to college are compared. The research shows the following: the application of the teaching implementation plan of personalized needs from the perspective of Kano can help improve the comprehensive performance of students; differentiated students can reduce differences through the teaching implementation plan of personalized needs from the perspective of Kano, so that personalized needs are more satisfied, so as to further improve the quality of teaching.
Prediction Model of Rotor Yarn Quality Based on CNN-LSTM
Authors: Hu Zhenlong
Published: August 2022

In the whole textile industry chain, yarn production is one of the key links, which has a great impact on the quality of textile and clothing products. For a long time, the textile industry has been hoping for a yarn quality prediction technology, which can accurately predict the final yarn quality indicators according to the known conditions such as raw materials and production processes. CNN-LSTM yarn prediction model is a deep neural network model based on the assumption that the influence of textile processing time series on yarn quality is considered. CNN optimizes the input eigenvalues through one-dimensional convolution and pooling, and LSTM matches the optimized fiber performance indexes and process parameters in time series according to the processing sequence and excavates their laws, thus realizing the goal of predicting yarn quality indexes. The effects of input fiber performance index, process parameters, convolution kernel parameters, pool kernel parameters, LSTM unit number, LSTM layer number, and optimization algorithm on prediction accuracy were studied, and the parameters of CNN-LSTM model were determined. Experiments on the data set of spinning yarn show that the mean square error (MSE) of CNN-LSTM model in predicting yarn strength, Dan Qiang unevenness, evenness unevenness, and total neps is lower than that of linear regression model and BP neural network. At the same time, it is found that the prediction accuracy of CNN-LSTM model is greatly influenced by process parameters and optimization algorithm.
Design and Analysis of Hybrid Fixed-Wing Type Flying Robot
Authors: Maity Ritu , Mishra Ruby , Patnaik Prashant Kumar , Sain Mangal
Published: August 2022

With the advent of disruptive technologies, unmanned aerial vehicles have seen substantial growth over the past few years. The market for flying robots is increasing drastically, and they are getting used in various sectors. This paper is aimed at discussing the novel design of a hybrid fixed-wing type flying robot in which both fixed-wing and rotary-wing concepts are combined so that stable flight with vertical takeoff can be possible. Our design proposes a compact structure that can be efficiently used in indoor applications. We have also discussed its structural analysis, the model’s stability, the CFD analysis, and the vibrational analysis of the designed structure. The objective is to design an effective compact flying robot system that will be used for medical applications and need to carry a payload of a minimum of 2 kg with good aerodynamic performance. The aerodynamic model required for a hybrid fixed-wing type flying robot has been developed, and the static stability of the model has been evaluated in the paper.
Charging-Related State Prediction for Electric Vehicles Using the Deep Learning Model
Authors: Zhao De , Wang Hua , Liu Zhiyuan
Published: August 2022

Electric vehicles (EVs) are becoming the potential contender for the conventional gasoline vehicles in view of the environment-friendly and energy-efficient characteristics. The prediction of EV charging-related states (defined in this study as home charge, outside charge, home stop, outside stop, low-battery travel, and high-battery travel) could help to identify the future charging demand (power consumption) of EV individuals. Specifically, it could guide the operation and management of charging facilities and also provide tailored charger availability information based on users’ real-time locations. This study aims to predict charging-related states of individual EVs using a deep learning approach. We first propose a tangible approach to convert EV trajectory data into state sequences and then develop a bidirectional gated recurrent unit model with attention mechanism (Bi-GRU-Attention) to forecast EV states. A sensitivity analysis is conducted to tune and/or calibrate parameters in the model based on plug-in hybrid EV trajectories dataset collected in Shanghai, China. Experiment results show that (i) the proposed method could achieve an average accuracy of 77.15% with a 1-hour prediction length and it outperforms the baseline models for all tested prediction lengths; (ii) it is also revealed that the prediction accuracy varies dramatically with different states and time periods. Among all states, the proposed model has a higher prediction accuracy on “home stop” (89.0%). As for time periods, the EV states around 08:00 am and 04:00 pm are hard to predict, and a comparatively low prediction accuracy (close to 60%) is obtained; and (iii) the stability and robustness analysis implies that the proposed model is stable and insensitive to SOC noise or season.
Similar Simulation Test on Deformation Characteristics of Overlying Rock Developed by Instability of Strip Coal Pillar
Authors: Liu Yong , Chen Bing , Cui Shilei , Yin Dawei
Published: August 2022

In order to protect surface buildings and maximize the recovery of coal resources, there are a large number of strip coal pillars and unfilled goafs left in strip mining areas in China. Affected by the comprehensive factors such as long-term overlying rock load, water accumulation in goafs, and adjacent mining, the instability probability of strip coal pillar increases greatly, which becomes a potential hidden danger of surface disaster. However, the law of overlying rock and surface movement caused by strip coal pillar instability is not clear up to now. Therefore, a series of experimental studies on the law of overlying rock movement and deformation induced by strip coal pillar instability are carried out in this paper. The results show that it is feasible to use paraffin-containing materials to make a strip coal pillar model and simulate strip coal pillar instability by heating paraffin. After mining, the subsidence curve of the L5 monitoring line and its upper strata is “disk.” After the instability of the strip coal pillar, the subsidence curve changes from “disk” to “basin.” The instability of strip coal pillar can lead to significant and more harmful overburden movement between overburden and surface than that of strip working face. However, because the unstable coal pillar still has a certain bearing capacity, the maximum subsidence of the surface is still far less than that under the condition of longwall mining.
Nonlinear Bivariate Bernstein–Chlodowsky Operators of Maximum Product Type
Authors: Özalp Güller Özge , Acar Ecem , Kırcı Serenbay Sevilay
Published: August 2022

The positive nonlinear operators with maximum and product were introduced by Bede. In this study, nonlinear maximum product type of bivariate Bernstein–Chlodowsky operators is defined and the approximation properties are investigated with the help of new definitions. In this paper, it was aimed that the order of approximation obtained with the nonlinear maximum product type of operator sequences would be better than the degree of approximation of the known linear operator sequences.
Comparison of the Ameliorating Effects of Valsartan and Amlodipine on Vascular Endothelial Dysfunction and Oxidative Stress in Elderly Patients with Type H Hypertension
Authors: Zhang Lijuan , Yang Hua , Yang Ping
Published: August 2022

Objective. To explore whether long-term administration of valsartan or amlodipine can improve vascular endothelial function and reduce the production of reactive oxygen species in patients with H-type hypertension, so as to provide a reference for clinical treatment. Methods. A total of 82 elderly patients with type H hypertension who were admitted to our hospital from March to August 2017 were selected as the research subjects. The study included a 4-week continuous irrigation period followed by a 24-week randomized treatment period. Forty patients in the valsartan group and 42 in the amlodipine group were treated with 5 mg amlodipine or 80 mg valsartan for 24 weeks. Clinical efficacy, 24 h mean DBP, SBP, and 24HSBP, DBP coefficient of variation, cardiac fatty acid-binding protein (H-FABP), vascular pseudohemophilia factor (VWF), nitric oxide (NO), endothelium-dependent vasodilation function (FMD), nonendothelium-dependent vasodilation function (NMD), malondialdehyde (MDA), GSH-Px, and SOD levels were observed. Results. The total effective rate was 80.0% (32/40) in the valsartan group and 85.71% (36/42) in the amlodipine group, and the difference was not statistically significant (

P
>
0.05

). After 24 weeks of treatment, the 24 h mean SBP, SDP, 24HSBP, and DBP variation coefficients of the two groups were significantly decreased, and there was no statistical significance between the two groups (

P
>
0.05

). After treatment, the values of H-FABP, VWF, NO, and MDA in both groups decreased compared with before treatment, while the values of FMD%, NMD%, SOD, and GSH-Px increased compared with before treatment (

P
>
0.05

), and the levels of H-FABP, VWF, and NO in the valsartan group were lower than those in the amlodipine group. Meanwhile, FMD%, NMD%, SOD, and GSH-Px levels were higher than those in the amlodipine group (

P
>
0.05

). Conclusion. Valsartan and amlodipine reduce blood pressure, improve vascular endothelial function, and inhibit oxidative stress in elderly patients with H-type hypertension on average. However, valsartan has a better effect on vascular endothelial dysfunction and oxidative stress in elderly patients with H-type hypertension.
Monte Carlo-Based Analysis and Experimental Validation of the Interception-Damage Probability of the New Active Interception Net
Authors: Liu Yiming , Xiong Ziming , Wang Jian , Wang Derong
Published: August 2022

This study aims to prove the intercepting effectiveness of the new active interception net. According to the timing sequence of intercepting the incoming missile body by the new interception net, the external ballistic equations of the incoming missile body and the interception net are established, respectively, and then the mathematical probability model conforming to the physical characteristics of the interception process is constructed. The process of intercepting the incoming missile by intercepting net is simulated 1000 times by Monte Carlo method on the basis of considering the random parameters such as route shortcut, dive angle, and incoming velocity of the incoming missile. The simulation results under different intercept conditions, including the optimal intercept angle, the power of the payload warhead, and the probability of intercept damage, are analyzed. Through analysis, it is found that with the cooperation of multiple interception nets, the probability of interception and damage to the incoming target can be significantly improved. When the velocity of the incoming missile body is up to 900 m/s, the probability of damage is over 65%. The research content theoretically verifies the effectiveness of ultra-short-range interception of the new active interception net. In order to further verify the reliability of interception, a new interception net was carried out to verify the interception of the incoming missile body. Through the analysis of the test results, it is found that the new interception net can intercept the incoming missile body effectively after launch, which well verifies the Monte Carlo simulation results and further proves the intercepting effectiveness of the new interception net.
On-Farm Composting of Agricultural Waste Materials for Sustainable Agriculture in Pakistan
Authors: Hashim Sarfraz , Waqas Muhammad , Rudra Ramesh P. , Akhtar Khan Alamgir , Mirani Asif Ali , Sultan Tariq , Ehsan Farrukh , Abid Muhammad , Saifullah Muhammad
Published: August 2022

Agriculture is the economic backbone of Pakistan. 67% of country’s population resides in rural areas and primarily depends on agriculture. Pakistan's soils are poor in OM and have a low C : N ratio, and the overall fertility status is insufficient to support increased crop yields. Compost is an excellent alternative solution for improving soil OM content. However, this excellent alternative supply in Pakistan has yet to be used. Mass volumes of leaves, grass clippings, plant stalks, vines, weeds, twigs, and branches are burned daily. In this study, different compost piles (P1, P2, and P3) of compost were made using different agricultural and animal waste combinations to assess temperature, pH, and NPK. Results revealed that P3 demonstrated the most successful composting procedure. The temperature and pH levels throughout the composting process were determined in a specified range of 42–45oC and 6.1–8.3, respectively. Total nitrogen content ranged from 81.5 to 2175 ppm in farm compost. Total phosphorus concentrations range from 1.33 to 13.98 ppm, and potassium levels, on the other hand, range from 91.53 to 640 ppm in farm compost. The overall nitrogen concentration grew progressively between each pile at the end of a week. The varied concentrations revealed that adding various forms of agricultural waste would result in a variation in the quantity of NPK owing to microbial activity. On-farm composting has emerged as an effective technique for the sustainability of agricultural activities, capable of resolving crucial problems like crop residues and livestock waste disposal. Based on this study’s results, the pile (P3) combination shows the best NPK value performance and is recommended for agricultural uses to overcome the OM deficiency.
Establishment and Application of Stochastic Mesoscopic Concrete Model
Authors: Wang Shufang , Zhai Xiaoliang , Gao Yangyang , Li Jialu , Hu Wei
Published: August 2022

Concrete is a kind of complex multiphase composite material, and the spatial distribution of each component of concrete is random, which has an impact on its physical and mechanical properties. This paper presents algorithms for generating two‐dimensional (2D) and three‐dimensional (3D) arbitrarily graded stochastic concrete aggregate models, graphical format conversion methods, and finite element applications. Aggregate shapes include circular, ellipsoidal, convex polygon, ellipsoidal, and spherical. These models take into account the randomness of appearance, position distribution, and particle size distribution, including the interface transition zone, and the aggregate content can reach a high level. An efficient interference algorithm of ellipsoidal aggregate is proposed, which can reasonably control the thickness of the mortar layer around the aggregate. The corresponding program is compiled by MATLAB to realize the generation of random mesoscopic concrete aggregate geometric model. A graphic format conversion method is provided, which greatly extends the applicability and generality of the random aggregation model. The usefulness of the method in this paper was verified by the mechanical analysis of random aggregate model specimens with different volume fractions and the chloride transport properties of different aggregate shape specimens. In addition, the results of the two cases show that the random aggregate geometry and aggregate content have a great impact on the physical and mechanical properties of concrete, and the randomness of the spatial distribution of aggregates makes the chloride ion transport path and the elastic modulus and Poisson’s ratio of concrete be random. The results of this paper can provide an important research foundation for the study of mesoscopic physical and mechanical properties, gradation theory, and durability of concrete.
CD96 as a Potential Diagnostic Biomarker and  New  Target for Skin Cutaneous Melanoma
Authors: Zhou Wangying , Cai Xiaobin , Liu Feng
Published: August 2022

Skin cutaneous melanoma has high morbidity and mortality. Identification of reliable and quantitative melanoma biomarkers could facilitate an early diagnosis and improve survival and morbidity rates. CD96 has a significant role in adjusting immune function. Although the abnormal expression of CD96 has been reported to participate in carcinogenesis in many human types of cancer, the bioinformatics role of the CD96 in melanoma is unknown. Expression degrees and their underlying functions were first studied by this study. According to TCGA, GTEx, and gene expression profile interaction analysis dataset in this paper, compared with normal skin tissues, CD96 was expressed at higher levels in human cutaneous melanoma skin tissues. Meanwhile, we detected the relative CD96 expression levels by immunohistochemistry. Gene functional enrichment analyses were applied through cBioPortal database analysis. CD96 was clearly upregulated in skin cutaneous melanoma patients and carried out its effects through regulating several signaling pathways, containing the JAK‐STAT, PI3K‐Akt, and MAPK. Taken together, the analysis results indicated that CD96 could be used as a new clinical bioindicator as well as an underlying medicinal target for cutaneous melanoma.
Diagnostic and Prognostic Impact of Progesterone Receptor Immunohistochemistry: A Study Evaluating More Than 16,000 Tumors
Authors: Viehweger Florian , Tinger Lisa-Marie , Dum David , Gorbokon Natalia , Menz Anne , Uhlig Ria , Büscheck Franziska , Luebke Andreas M. , Hube-Magg Claudia , Hinsch Andrea , Höflmayer Doris , Fraune Christoph , Lebok Patrick , Weidemann Sören , Lennartz Maximilian , Jacobsen Frank , Clauditz Till S. , Krech Rainer , Krech Till , Marx Andreas H. , Simon Ronald , Burandt Eike , Steurer Stefan , Sauter Guido , Minner Sarah , Bernreuther Christian
Published: August 2022

Progesterone receptor (PR) is a member of the nuclear/steroid hormone receptor family of ligand-dependent transcription factors. It plays an important role in reproduction and mammary gland development and has various tissue-specific effects in nonreproductive organs. In diagnostic pathology, positive PR immunostaining is used to support a diagnosis of breast or gynecologic origin in a tumor. In this study, the expression of PR was analyzed by immunohistochemistry in 18,176 (interpretable: 16,445) samples from 147 different tumor types and subtypes in a tissue microarray format. PR immunostaining was detected in 57.4% of breast tumors, 28.6% of other gynecological tumors, and 1.8% of nongynecological and nonmammary tumors. Among the group of nongynecological and nonmammary tumors, particularly high rates of PR positivity were seen in neuroendocrine tumors (54.3%) and neuroendocrine carcinomas (35.7%) of the pancreas. A comparison with clinico-pathological parameters showed that reduced PR immunostaining was significantly associated with adverse histopathological and clinical features in breast carcinoma, endometrioid endometrial carcinoma, and pancreatic neuroendocrine tumors. In summary, our analysis of 147 different tumor types for PR immunostaining provides a ranking list of tumor entities according to their prevalence of PR positivity, helps to better understand the diagnostic utility of PR, and highlights the distinct PR positivity among neuroendocrine neoplasms of pancreatic origin.
The Relationship between Human Capital and Economic Growth beyond Logarithmic Production Function
Authors: Geng Hongmei
Published: August 2022

The problem that the relationship between human capital and industrial development goes beyond the logarithmic production function is solved, the needs of human capital’s understanding of the economic development process and the lack of understanding of the relationship between human capital and development are met, and the understanding of the relationship between human investment and business development is improved. Based on the new characteristics of industry development and population emergence, this paper adopts a functional model with no further elasticity of logarithmic production, resulting in an empirical analysis of national development observations based on Chinese statistics and beyond logarithmic performance. From 2003 to 2016, 31 finance scholars were interviewed to examine the human capital process of economic development since the new century. The first is to establish a theoretical model of industry development including human capital, operations, and capital resources; the second is to establish human capital assessment tools including education, health, technology, and population migration to determine the accuracy of the measurement system. Then, the main concepts of regression analysis are used to describe the relationships or interrelationships of various factors and the development of an industry. The results show that since the beginning of the new century, my country’s human capital generally includes two major themes: “education and scientific research human capital” reflects the well-being of human beings, among which “public human capital” is the main one. Won adopts a two-stage trans-logarithmic calculation and believes that South Korea’s strength and capital change one by one, and the elasticity values are 0.78 and −1.42, respectively, when added to the performance. While the growth of the economy has not been separated from the increase of human and capital, the best way is human capital, which means more than population growth.
Physical Layer Security for CRNs over Beaulieu-Xie Fading Channels
Authors: Zeng Yanyang , Hua Yang , Fang Yiwei , Wang Xiaohong
Published: August 2022

In recent years, the access of massive communication devices leads to the insufficient spectrum resources of wireless networks. One of the practical means to resolve the problem is to build cognitive radio networks (CRNs), which can realize the sharing of spectrum resources between primary and secondary users, thereby improving the utilization rate of wireless spectrum resources. To this end, the CRNs are utilized to establish the Wyner’s eavesdropping model over the Beaulieu-Xie fading channels. We mainly deduce the accurate expressions of secrecy outage probability and strictly positive secrecy capacity to explore the performance of physical layer security. Moreover, the better overlap between the statistical simulation and the theoretical results indicates the correctness of the theoretical analysis equation. The interesting results are that both increasing the

P

max

and decreasing the

C

t
h

can improve the security performance. This work is a good reference and guidance for modeling CRNs (Internet of Things, fifth generation, cell phone networks, etc.) and security performance evaluation.
Analysis of the Curative Effect and Prognostic Factors of Anterior Cervical Surgery for Spinal Cord Injury without Radiographic Abnormalities
Authors: Tan Jian , Hu Fanchen , Ou Jun , Su Xiaotao , Liu Jingnan
Published: August 2022

Objective. The study aimed to investigate the effect of anterior cervical surgery in the treatment of spinal cord injury without radiographic abnormalities (SCIWORAs) and analyze the related factors affecting the prognosis of patients. Methods. A total of 86 patients with SCIWORA who were admitted to our hospital from June 2018 to March 2021 were selected as the research subjects. According to the different treatment methods selected by the patients, they were divided into the control group (n = 38) and the observation group (n = 48). The control group was treated with conservative therapy, and the observation group was treated with anterior cervical total laminectomy decompression, internal fixation, and bone graft fusion. The efficacy of the treatment was assessed preoperatively and 6 months after surgery using the Japanese Orthopedics Association (JOA) functional evaluation criteria for cervical spinal cord injury. The improvement rate of the JOA score at the last follow-up visit was calculated according to the Hirabayashi formula to evaluate the prognosis of patients. Results. The JOA score of the observation group six months after surgery was (14.98 ± 2.75) that was higher than that of the control group (12.16 ± 2.54) (

P
<
0.05

). After surgery, the improvement rate of the JOA score in the observation group was higher than that in the control group (

P
<
0.05

). After surgery, the scores of health condition, physiological function, and role physical in the observation group were (23.18 ± 1.09), (22.75 ± 1.54), and (22.64 ± 1.46), which were higher than those in the control groups (20.94 ± 1.65), (20.26 ± 1.78), and (19.56 ± 1.82) (

P
<
0.05

). The results of univariate analysis showed that the ASIA classification of cervical spinal cord injury, the type of MRI cervical spinal cord injury, the scope of cervical spinal cord injury, lumbar disc herniation, and the time from injury to treatment were all related to the prognosis of the patients (

P
<
0.05

). Multivariate analysis showed that the ASIA classification of cervical spinal cord injury, the type of MRI cervical spinal cord injury, the scope of cervical spinal cord injury, and the time from injury to treatment were the independent factors affecting the prognosis of patients (

P
<
0.05

). Conclusion. For patients with SCIWORA, anterior total lamina decompression and internal fixation with bone grafting and fusion can effectively promote the recovery of cervical spinal cord function and improve the prognosis and quality of life of patients. The ASIA classification of cervical spinal cord injury, the type of MRI cervical spinal cord injury, the scope of cervical spinal cord injury, and the time from injury to treatment were the independent prognostic factors for patients.