bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis and Validation of Differentially Expressed Ferroptosis-Related Genes in Regorafenib-Induced Cardiotoxicity
Authors: Zhang Siyuan , Xu Xueming , Li Zhangyi , Yi Tian , Ma Jingyu , Zhang Yan , Li Yilan
Published: September 2022

Background. Although tyrosine kinase inhibitors (TKIs) constitute a type of anticancer drugs, the underlying mechanisms of TKI-associated cardiotoxicity remain largely unknown. Ferroptosis is a regulated cell death form that implicated in several tumors’ biological processes. Our objective was to probe into the differential expression of ferroptosis-related genes in regorafenib-induced cardiotoxicity through multiple bioinformatics analysis and validation. Methods and Materials. Four adult human cardiomyocyte cell lines treated with regorafenib were profiled using Gene Expression Omnibus (GEO) (GSE146096). Differentially expressed genes (DEGs) were identified using DESeq2 in

R

(V.3.6.3). Then, Gene Ontology (GO) Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis, and Gene Set Enrichment Analysis (GSEA) were used to explore DEGs’ bioinformatics functions and enriched pathways. We intersected DEGs with 259 ferroptosis-related genes from the FerrDb database. Finally, the mRNA levels of differentially expressed ferroptosis-related genes (DEFRGs) were validated in regorafenib-cultured cardiomyocytes to anticipate the link between DEFRGs and cardiotoxicity. Results. 747,1127,773 and 969 DEGs were screened out in adult human cardiomyocyte lines A, B, D, and E, respectively. The mechanism by which REG promotes cardiotoxicity associated with ferroptosis may be regulated by PI3K-Akt, TGF-beta, and MAPK. GSEA demonstrated that REG can promote cardiotoxicity by suppressing genes and pathways encoding extracellular matrix and related proteins, oxidative phosphorylation, or ATF-2 transcription factor network. After overlapping DEGs with ferroptosis-related genes, we got seven DEFRGs and found that ATF3, MT1G, and PLIN2 were upregulated and DDIT4 was downregulated. The ROC curve demonstrated that these genes predict regorafenib-induced cardiotoxicity well. Conclusion. We identified four DEFRGs which may become potential predictors and participate in the regorafenib-induced cardiotoxicity. Our findings provide possibility that targeting these ferroptosis-related genes may be an alternative for clinical prevention and therapy of regorafenib-related cardiotoxicity.
Application and Analysis of Bolt Support in Mine Driving Roadway
Authors: Cao Feifei , Fang Tao
Published: September 2022

Mineral resources are one of the main energy sources used in industrial production. While the economy is going up, the mining of coal mines and other mineral resources has also intensified. With the deepening of coal mining, the difficulty of mining has also increased. In order to ensure the safe production of relevant enterprises, it is necessary to vigorously apply the supporting technology of mineral resources excavation to reduce the occurrence of accidents in the mining area. Based on this, this paper, on the basis of collecting the geological conditions of a mine, combined with the stability theory of roadway surrounding rock, the existing bolt (anchor cable) support theory, and the factors affecting the reliability of bolt support, analyzed the bolt (anchor cable) support principle and deduced and calculated the support capacity of a single bolt and a group of bolts in the process of mining excavation through the energy conservation theorem. Through Mohr-Coulomb strength theory and limit line analysis theory, a reasonable mechanical model is established, and it is concluded that the ultimate bearing capacity of the bolt will increase with the increase of the length of the anchorage section no matter what support method is used for roadway surrounding rock support, and the anchorage length and ultimate bearing capacity have a nonlinear positive correlation. However, the ultimate supporting capacity of the suspension bolt cannot meet the actual work demand, while the composite beam and composite arch bolt support meet the conclusion. In addition, according to the conclusion and the suggestion that the design of bolt support mode should start with the study of the mechanism of bolt support on the roadway, the application of bolt support mode in the resource mine heading roadway is comprehensively analyzed.
Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms
Authors: Xiao Yineng
Published: September 2022

The age of algorithms is here, and it is really changing people’s lives. More and more ethical problems related to algorithms have attracted people’s attention, but the related ethical research is still far behind the research of algorithms. As more intelligent algorithms emerge in an endless stream, there will also be a lot of algorithmic ethical issues. On the other hand, with the continuous improvement of the development level of the automobile industry, people have a stronger demand for the safety and stability of modern transportation, and more and more autonomous driving technology has been promoted and applied in the market. At present, most of the studies on the longitudinal collision avoidance system of vehicles use collision warning or emergency braking to avoid collision. However, when the vehicle is in a special situation such as high speed and slippery road, emergency steering is more effective. In order to further improve the vehicle safety and ethical algorithm design points, this article revolves around vehicle lateral active collision avoidance control method research, the collision avoidance decision-making, and path planning and collision avoidance transverse vehicle longitudinal motion control is analyzed, and based on automated driving simulation experiment, the tests carried out to verify the designed control strategy. The experimental results show that the proposed method not only has a good effect of preventing automatic driving collision but also can meet the requirements of algorithm ethics. This research can effectively guide the research of algorithmic ethics in the field of autonomous driving and effectively reduce the occurrence of traffic accidents.
Estimation and Confidence Intervals of a New PCI CNpmc for Logistic‐Exponential Process Distribution
Authors: Alotaibi Refah , Dey Sanku , Saha Mahendra
Published: September 2022

The process capability index has been introduced as an effective tool used in industries to aid in the assessment of process performance as well as to measure how much the product meets the costumer expectations. We are aware that classical process capability indices provide better results when the quality characteristic of the processes follows normal distribution. However, these classical indices may not provide accurate results for evaluating nonnormally distributed process which in turn may hinder the decision‐making. In this article, we consider a new process capability index CNpmc which is based on cost function and is applicable both for normally and nonnormally distributed processes. In order to estimate the process capability index CNpmc when the process follows logistic‐exponential distribution, we have used ten classical methods of estimation, and the performances of these classical estimates of the index CNpmc are compared in terms of their mean squared errors through a simulation study. Next, we construct five bootstrap confidence intervals of the process capability index CNpmc and compare them in terms of their average width and coverage probabilities. Finally, two data sets related to electronic industries are reanalyzed to show the applicabilities of the proposed methods.
Teaching Quality Evaluation of Animal Science Specialty Based on IPSO-BP Neural Network Model
Authors: Chen Liyan , Wang Lihua , Zhang Chunyou
Published: September 2022

Teaching quality evaluation is one of the most commonly used educational evaluation methods, which is used to evaluate teachers’ teaching ability and teaching effect. In order to improve the effectiveness and accuracy of teaching quality evaluation, a BP neural network model based on improved particle swarm optimization (IPSO) is proposed. Firstly, the evaluation index system of teaching quality is constructed with teaching attitude, teaching content, teaching method, and teaching effect as indicators. Then, IPSO algorithm is used to optimize the weight and threshold of neural network to improve the performance of BP algorithm. Secondly, IPSO-BP algorithm is used for sample training to optimize the model structure. Finally, the model is used to evaluate the teaching quality of animal science-related courses in Inner Mongolia University for Nationalities. The results show that compared with the ordinary BP neural network model, the IPSO-BP model has fast convergence speed, good robustness, and strong global search ability, and the evaluation accuracy rate is 96.7%. It is feasible in the evaluation of teaching quality.
Analysis of the Relationship between Vehicle Behaviors of Changing Lane and Volume of Traffic under Different Operating Ratios of Autonomous Vehicles
Authors: Xie Han , Li Kehong , Zhu Juanxiu
Published: September 2022

The lane-changing behavior is one of the important causes in traffic accident in congest traffic, and many behaviors of change lane affect volume of traffic. When autonomous driving vehicles are running on road with human-driven vehicles, the effects of change lane on traffic are different. In all human-driven vehicles traffic, the vehicle behaviors of changing lane are more competent. When autonomous driving vehicles are running in mixed traffic, the behaviors of changing lane decrease and the volume of traffic increases. However, a few studies have involved the relationship between traffic volume and lane-change behavior. In a sense, the study of this relationship is good for understanding the operation mechanism of mixed traffic. In this paper, we proposed the linear regression model to describe the relationship between traffic volume and lane-change behavior. The model can be used to establish the basic graph model. Here, we used empirical, simulation, and data-driven methods to obtain data and established a multiple linear regression model. First, we empirically study the continuous traffic with all human-driven vehicles. Then, the corresponding simulation model is established, and the availability of the simulation model is proved by data comparison with empirical study. Finally, 9 rounds of simulation experiments are carried out with the established simulation model. The number of autonomous driving vehicles in each round of simulation experiment increases by 10%. Then, we analyze the data of the behaviors of changing lane and the volume of traffic from simulation experiments. The following was found: (1) an increase in autonomous driving vehicle leads to an increase in traffic volume and a slight decrease in lane changing behaviors; (2) the influence of different proportions of autonomous vehicles on the traffic volume of lanes at different locations is slightly different; and (3) the relationships among the rate of vehicles entering lane, the rate of vehicles exiting lane together, and the volume of traffic show obvious linear relationships with the increase in autonomous driving vehicles. We used multiple linear regression models to carry out description, and the obtained parameter value intervals are close under different operating ratios of autonomous vehicles. To sum up, on multilane roads, especially 4-lane urban expressways, autonomous driving vehicles join in the traffic, which can effectively increase the volume of traffic of each lane while reducing vehicle behaviors of changing lane. The relationships between vehicle behaviors of changing lane and the traffic volume in mixed traffic show linear relationships with the increase in autonomous driving vehicles. In the future, we will further study whether this relationship model can be used in discrete traffic flow.
Molecular Hydrogen Reduces Electromagnetic Pulse-Induced Male Rat Reproductive System Damage in a Rodent Model
Authors: Ma Long , Hao Wei , Feng Wen-Bo , Cao Lu-Cheng , Qin Li-Na , Wang Yao , Liu Ming-Hua , Wang Neng-Zhuang , Gao Fu , Guo Jia-Ming , Du Hui , Yan Hong-Li
Published: September 2022

Infertility has got to be a broadly concerned social issue these days, in which the malefactor cannot be overlooked. Numerous studies have shown that electromagnetic pulse (EMP) radiation may have seriously damaging effects on reproductive health, through nonthermal effects and oxidative stress. Molecular hydrogen, a selective hydroxyl radical scavenger, explains the protective effects against many diseases closely associated with oxidative damage, such as ionizing radiation (IR). We sought to characterize the beneficial effects of molecular hydrogen on the male reproductive system in a rodent EMP exposure model. The 8-week-old male Sprague-Dawley rats were exposed to EMP (peak intensity 1000 kV/m, pulse edge 20 ns, pulse width 200 ns, 1 Hz, and 200 pulses), with or without hydrogen-rich water. The pathological structure of the testis, the rate of apoptosis of the testis, the serum testosterone level, the sperm parameters, and the activity of the antioxidant enzymes of the testis were measured. Then, transcriptomic and untargeted metabolomic analyses were applied to uncover the underlying mechanism. Exposure to EMP increased testicular apoptosis rate and apoptosis protein level, decreased sperm viability and motility, decreased serum testosterone levels, and diminished testicular antioxidant capacity. Molecular hydrogen-alleviated damage decreased the testicular apoptosis rate and apoptosis protein level, increased sperm motility, increased serum testosterone levels, and improved antioxidative capacity. Omics results showed that molecular hydrogen has a strong influence on metabolic pathways, and EMP affects mainly oxidative phosphorylation, TNF signaling pathways, and cytokine-receptor interactions. The mechanism of molecular hydrogen’s effect may be related to the reversal of some metabolite levels. These observations warrant molecular hydrogen as an innovative approach for potential protection against EMP.
Effect of New Media Communication on Consumer Behavior Based on Industrial Edge Cloud Deployment Algorithm
Authors: Xie Nijia , Ren Lili
Published: September 2022

In the era of advanced information technology, new media represented by mobile phones have the characteristics of fast information dissemination, unlimited time, and place, and they are loved by people. Just like this, many businesses and enterprises use new media tools to promote and sell product information, and they have achieved positive results. In this context, this paper mainly studies the impact of new media communication on consumer behavior. By combining the characteristics of the industrial edge cloud deployment algorithm, an analysis model of behavior influencing factors is constructed, and relevant analysis is carried out. This paper mainly obtains experimental data by means of questionnaires and conducts experimental demonstrations for the proposed hypotheses. Before the experiment, the algorithm is compared and analyzed, and it is concluded that when the production line is 50, the algorithm in this paper is 0.6% and 0.32% lower than other algorithms under the SER index. When the production line is 100, the algorithm in this paper is 9.9% and 6.3% lower than other algorithms under the ELDR index. When the production line is 150, the algorithm in this paper is 2.2% and 2.4% lower than other algorithms under the ACR index, indicating that the performance of the algorithm in this paper is better than other algorithms under the SER, ELDR, and ACR indexes. In the correlation and regression analysis of variables, the results show that there is a positive relationship between consumer behavior and consumer willingness. At the same time, there is a positive relationship between consumer willingness and perceived novelty, perceived value, perceived interactivity, and perceived usefulness of new media. The coefficients of its functional expression are 0.411, 0.378, 0.241, and 0.216, respectively, verifying the assumptions 3, 5, 7, and 8 hold. It shows that consumer behavior is affected by consumer willingness, and consumer willingness is most affected by the perceived novelty of new media, followed by new media perceived value, perceived interactivity, and perceived usefulness.
Hollow Fibre-Supported Liquid Membrane for the Determination of Sulfonamide Residues in Egg Samples
Authors: Bekele Eskinder Teklu , Dube S. , Nindi M. M.
Published: September 2022

In this study, a three-phase hollow fibre-supported liquid membrane (HF-SLM) technique incorporated with high-performance liquid chromatography (HPLC) coupled with diode array detection (DAD) was developed for the extraction, clean-up, and determination of fifteen sulfonamide residues in chicken egg samples. The residues were extracted from the 5 mL sample solution of pH 2.5 into a thin layer of organic phase (1-octanol with 10% TOPO) immobilised in hollow fibre pores and then back-extracted into approximately 6 μL of aqueous phase (pH 13) located in the lumen of the hollow fibre. After extraction, 6 μL of the acceptor phase was injected into an HPLC instrument for subsequent analysis. Under optimum conditions, the limit of detection (LOD) and limit of quantification (LOQ) values ranged from 0.8–7.9 μg·kg−1 and from 2.4–21.0 μg·kg−1, respectively, linearity in the range of 5 1 000 μg·kg−1, and intra- and inter-day precision (%RSD) values at three concentration levels (50, 100, and 500 μg·L−1) ranged from 6.2–15.7%, 7.3–15.0%, and 7.3–14.6%; and 6.4–17.4%, 4.3–16.2%, and 8.3–16.5%, respectively, were obtained. The accuracy of the method, expressed as percentage recovery, was in the range of 71.0–98.7%, with corresponding %RSD (n = 6) values ranging from 1.9–9.9% being obtained. The developed method provided enrichment factors in the range of 17.1 to 541.4. The applicability of the proposed method was also evaluated by analysing egg samples, which were randomly collected from local supermarkets located in Gauteng Province, South Africa. The results obtained revealed that the developed method has the potential to be used as an alternative method for the determination of sulfonamide residues in egg and related complex samples.
Experimental Study on Compressive and Flexural Performances of Polypropylene Fiber-Reinforced Concrete
Authors: Wei Yimeng , Qin Yuan , Chai Junrui , Xu Chengyong , Zhang Yue , Zhang Xianwei
Published: September 2022

Plain concrete (PC) has the disadvantages of easy cracking and low resistance to deformation. In practical engineering, steel fiber or polymer fiber is usually selected to improve the tensile and crack resistance of concrete. In this study, the polypropylene fiber (PPF) was added to the concrete with the content of 0, 0.6, 0.9, and 1.5 kg/m3. The compressive and flexural tests of PC and PPF-reinforced concrete (PPFRC) at 14, 28, and 60 days of curing were conducted. As the result, when the content of PPF exceeds to 1.2 kg/m3, the workability became worse. The compressive and flexural strength of PPFRC increased compared with PC. However, the increase of strength does not always increase with the increase of fiber content. The uniaxial compressive strength of the PPFRC with the PPF of 1.2 kg/m3 is the optimal at 14- and 28-day ages, which is 20.56% and 11.24% higher than PC, respectively. PPFRC with the PPF of 0.9 kg/m3 is best of all at 60 days, which is 19.68% higher than PC. The flexural strength of the PPFRC-0.9 is the highest. Furthermore, the CFEC and CTI of PPFRC are both higher than those of PC, indicating that it has significant crack resistance and toughness.
An Existence Study on the Fractional Coupled Nonlinear
q
-Difference Systems via Quantum Operators along with Ulam–Hyers and Ulam–Hyers–Rassias Stability
Authors: Rezapour Shahram , Thaiprayoon Chatthai , Etemad Sina , Sudsutad Weerawat , Deressa Chernet Tuge , Zada Akbar
Published: September 2022

In this paper, we study the existence of solutions and their uniqueness and different kinds of Ulam–Hyers stability for a new class of nonlinear Caputo quantum boundary value problems. Also, we investigate such properties for the relevant generalized coupled

q

-system involving fractional quantum operators. By using the Banach contraction principle and Leray-Schauder’s fixed–point theorem, we prove the existence and uniqueness of solutions for the suggested fractional quantum problems. The Ulam–Hyers stability of solutions in different forms are studied. Finally, some examples are provided for both

q

-problem and coupled

q

-system to show the validity of the main results.
The Game and Impetus Analysis of Ecological Enrichment of Corporate Citizens
Authors: Li Wanting , Ye Yongling , Ning Yumei
Published: September 2022

Ecological enrichment is not the growth of the economic value of a business entity and the use of surplus to fund deficits, but the coordinated growth of corporate citizens’ ecological value and economic value, which is conducive to the positive evolution of ecological beauty and economic vitalization. Through the game model, this paper makes a game analysis of various possibilities of the game of ecological enrichment and further gives a balanced solution to the impetus of ecological enrichment. Positive‐sum return, return equilibrium, and dynamic equilibrium are all the necessary conditions for the positive selection of repeated games for corporate citizens’ ecological enrichment and play a decisive role in the impetus of ecological enrichment.
Optimization and Simulation of Saw Dust Ash Concrete Using Extreme Vertex Design Method
Authors: Ibe Iro Uzoma , Alaneme George Uwadiegwu , Milad Abdalrhman , Olaiya Bamidele Charles , Otu Obeten Nicholas , Isu Edward Uchenna , Amuzie Magnus Nnaemeka
Published: September 2022

In this research study, a constrained simplex optimization method was adapted for the evaluation of green concrete’s mechanical property constituting of two-component mixture problem of cement and saw dust ash (SDA), which is a derivative from industrial residue. This experiential research will provide avenue for the recycle and incorporation of waste materials to achieve sustainability and control indiscriminate disposal of waste. The formulated components constrained were realized from the relevant literature studies to obtain the feasible planes in the simplex, and from this computation approach using I-optimality, the design mixture proportions and experimental runs were derived. The experimental results obtained from the laboratory experiments showed a maximum compressive strength of 31.13 N/mm2 with a ratio of 0.875 : 0.125 for cement and SDA, respectively, and a flexural strength of 9.49 N/mm2 with a ratio of 1 : 0 for cement and SDA, respectively; the results were observed to decline linearly with the further addition of SDA. The details generated from laboratory program were utilized for the development of the EVD model through fits statistical evaluation, ANOVA, diagnostic test and influence statistics, numerical optimization, and graphical statistical computations to analyze the datasets and locate the optimal levels of mixture ingredients using desirability function. A desirability score of 0.990 was derived at a mix ratio of 0.89 : 0.110 for the two components, cement and SDA, to produce a maximized compressive strength and a flexural strength of 31.102 N/mm2 and 9.384 N/mm2, respectively. Furthermore, the test of adequacy of the generated EVD model was carried out through simulation and statistical validation exercises using the F-test and student’s t-test, and the outcome signified a good correlation between the EVD model-simulated and actual values withpvalue >0.05.
A Study on the Effects of Loading Axial Pressure Rate on Coal Mechanical Properties and Energy Evolution Law
Authors: Guo Hongjun , Sun Zhongguang , Ji Ming , Liu Dapeng , Nian Lihui , Wu Yongfeng
Published: September 2022

Due to the unique nature of the coal mining industry, safety is always one highly upheld premise of high production and high efficiency. According to the stress adjustment characteristics of the surrounding rocks in roadway excavation under a nondisruptive environment, the stress paths of unloading confining pressure and loading axial pressure were designed creatively and vividly, and the coal mechanical properties and energy evolution law under different loading axial pressure rates were studied through a series of experiments. As the loading axial pressure rate increases, the mechanical parameters at the time of coal failure show a nonlinear increase in the peak strength, the confining pressure, and the axial strain, while the variation laws of lateral strain and volumetric strain are not obvious. In addition, the failure mode transfers from the brittle failure to the ductile failure. In terms of energy, the positive work done by the axial pressure, the total work, and the elastic strain energy tend to increase nonlinearly, while the negative work done by the confining pressure and the plastic strain energy increase conversely. The elastic strain energy conversion rate increases logarithmically, indicating that a higher loading axial pressure rate tends to increase the probability and strength of coal instantaneous failure and subsequent dynamic behaviors. The research results reveal that providing an appropriate pressure relief and timely support after the roadway excavation in the actual production process can effectively reduce the energy level of the environment at the location of the surrounding rock support system, which is conducive to the roadway support and the surrounding rock stability control. Furtherly, it has important reference value for the roadway excavation and other underground engineering excavation and support operations and is of great significance to promote the development of deep resources.
Intelligent Action Recognition and Dance Motion Optimization Based on Multi-Threshold Image Segmentation
Authors: Han Yang , Mojtahe Kaza
Published: September 2022

In order to improve the recognition and optimization effect of dance movements, this paper combines multi-threshold image segmentation technology to perform intelligent recognition of dance movements. Moreover, this paper analyzes the window focus calibration process in the cascade multi-threshold expansion method and focuses the window on the object body to expand from small to large. Then, due to the local optimum problem of single-layer expansion, this paper proposes a cascade expansion method, and comparative experiments analyze the effect of this method on window optimization. In addition, this paper combines the actual situation of dance movement training to construct an intelligent action recognition and dance movement optimization system based on multi-threshold image segmentation. The research shows that the intelligent action recognition and dance motion optimization method based on multi-threshold image segmentation proposed in this paper has a good effect.
Hydrogeochemical Characteristics and Genesis of the Chazi Geothermal Field Area in Tibet
Authors: Song Mingzhong , Lang Xujuan , Zhong Zhennan , Kang Fengxin , Nan Dawa , Li Haoting , Yu Haowen , Liu Shaoyun , Han Sihang , Liu Zhao
Published: September 2022

The Chazi geothermal field area is located in the large region of Shigatse in southern Tibet. The geothermal resources in this area are abundant, but their exploitation and utilization are low. By studying the water chemistry and isotope characteristics of geochemical fluids in the study area, information on water chemistry, heat storage temperature, recharge source, recharge elevation, and circulation depth was obtained. These results provide a scientific theoretical basis for improving the genetic mechanism of high-temperature geothermal systems in the study area. The type of geothermal fluid hydrochemicals in this area is mainly HCO3–Na. The isotopic geochemical method was used to determine that the recharge source of geothermal fluids was atmospheric precipitation, and the recharge elevation was 5200–6000 m. The geochemical thermometer, Na–K–Mg equilibrium diagram, and silica-enthalpy mixed model indicated the shallow and deep thermal storage temperatures of approximately 150 and 200°C, respectively, and the average circulation depth of 1163.38 m in the study area. Combined with the fracture structure and magmatic activity characteristics of the southwest Qinghai-Tibet Plateau, the source, storage, cover, and general situation of the area were preliminarily summarised, and the conceptual model of geothermal origin was established. The results can provide a scientific theoretical basis for the mechanism of high-temperature geothermal systems and subsequent drilling and resource development.
Effect of Welding Sequence and Constraint on the Residual Stress and Deformation of Thick Welded Butt Joint Made of Q345qD Steel
Authors: Zhang Yi , Su Wenxue , Dong Hua , Li Tao , Cao Hongyou
Published: September 2022

In this paper, the residual stress and deformation of 32 mm thick welded butt joints made of Q345qD steel using gas metal arc welding (GMAW) are studied by using the thermoelastic‐plastic finite element analysis (FEA). Element birth and death technique is used to simulate the deposition of the welding consumables, and the volume heat source in double‐ellipsoidal distribution is used. An in‐house python code is developed to enable the volume heat source movement. After the finite element model was verified against the experimental results, the effects of five welding sequences and two types of boundary constraint conditions on the residual stress and deformation of the thick welded butt joints were investigated. Results of the experimental measurement and the numerical simulation are in good agreement. The transverse residual stress and the vertical deformation can be significantly reduced by increasing the alternating times of the welding beads in two halves of the weld. The transverse residual stress and the vertical deformation can be reduced by 21.4% and 87.4%, respectively. In addition, the residual stress and deformation of the thick welded butt joints are significantly affected by the applied constraints. Increasing the constraint significantly increases the overall level of the transverse residual stress but can decrease the maximum longitudinal residual stress by 51.8% and the vertical deformation by 65.9%.
High-Throughput 16 S rRNA Gene Sequencing Reveals Bacterial Diversity of Infant Formula Production Line Samples in Spring and Summer
Authors: Li Xian , Li Yongbo , Zhang Yalun , Zhang Rui , Wang Hui , Zhang Tao , Yan Taotao , Zhang Zilun , Shi Guohua , Zhou Wei
Published: September 2022

The trend of low breastfeeding rates increases the demand for infant milk formula (IMF) worldwide, but the use of IMF may be one of the causes of bacterial infections in infants. Complete sterility in the whole production line of IMF cannot be guaranteed; therefore, it is necessary to closely monitor the microbial content in the process. In the present study, an IMF powder production line based on the wet mixing process was sampled at 27 suspicious points in spring and summer to analyze the bacterial diversity by high-throughput sequencing. We found that 70 and 69 different bacterial phyla were present in spring and summer samples, respectively, with Proteobacteria and Firmicutes being the dominant phyla (>80% relative abundance). Moreover, 13 dominant genera each were present in spring (e. g., Pseudomonas and Lactococcus) and summer (e. g., Pseudomonas, Bacillus, and Streptococcus). Samples associated with workers showed higher bacterial species diversity (Shannon index) and richness (Chao1 index) in summer than in spring. The bacterial community composition showed high similarity between liquid milk after pasteurization and concentrated milk after evaporation. The potential bacterial pathogens were identified as Pseudomonas aeruginosa in spring and Acinetobacter baumannii in summer. Through retrospective analysis of the two opportunistic pathogens identified, it was found that the workshop environment was the potential contamination point in spring, whereas the auxiliary ingredients were the potential source of contamination in summer. The results highlight the effect of season on bacterial diversity associated with the production process of IMF and are useful in controlling the microbial quality and safety of infant dairy products.
Characterizations of Lifetime Distributions Using Two Relative Reliability Measures
Authors: Alomani Ghadah , Kayid Mohamed
Published: September 2022

In this paper, a general characterization property considering two new dynamic relative reliability measures is obtained. The new dynamic relative reliability measures are expressed as the ratio of hazard rates and as the ratio of reversed hazard rates. The measures are evaluated partially at some sequential random times following a specific distribution. We show that several particular statistics, as random times, fulfill that specific distribution, and thus, the result is applicable in the context of the specified random times. The results are applied to some examples to characterize the Weibull distribution and the inverse Weibull distribution.
Application of ARA-Residual Power Series Method in Solving Systems of Fractional Differential Equations
Authors: Qazza Ahmad , Burqan Aliaa , Saadeh Rania
Published: September 2022

In this research, systems of linear and nonlinear differential equations of fractional order are solved analytically using the novel interesting method: ARA- Residual Power Series (ARA-RPS) technique. This approach technique is based on the combination of the residual power series scheme with the ARA transform to establish analytical approximate solutions in a fast convergent series representation using the concept of the limit. The proposed method needs less time and effort compared with the residual power series technique. To prove the simplicity, applicability, and reliability of the presented method, three numerical examples are proposed and simulated. The obtained results show that the ARA-RPS technique is applicable, simple, and effective to get solutions to linear and nonlinear engineering and physical problems.
Intelligent Identification of Coal Crack in CT Images Based on Deep Learning
Authors: Yu Jinxia , Wu Chengyi , Li Yingying , Zhang Yimin
Published: September 2022

Automatic segmentation of coal crack in CT images is of great significance for the establishment of digital cores. In addition, segmentation in this field remains challenging due to some properties of coal crack CT images: high noise, small targets, unbalanced positive and negative samples, and complex, diverse backgrounds. In this paper, a segmentation method of coal crack CT images is proposed and a dataset of coal crack CT images is established. Based on the semantic segmentation model DeepLabV3+ of deep learning, the OS of the backbone has been modified to 8, and the ASPP module rate has also been modified. A new loss function is defined by combining CE loss and Dice loss. This deep learning method avoids the problem of manually setting thresholds in traditional threshold segmentation and can automatically and intelligently extract cracks. Besides, the proposed model has 0.1%, 1.2%, 2.9%, and 0.5% increase in Acc, mAcc, MioU, and FWIoU compared with other techniques and has 0.1%, 0.8%, 2%, and 0.4% increase compared with the original DeepLabV3+ on the dataset of coal CT images. The obtained results denote that the proposed segmentation method outperforms existing crack detection techniques and have practical application value in safety engineering.
The Prediction of Necroptosis-Related lncRNAs in Prognosis and Anticancer Therapy of Colorectal Cancer
Authors: Xiao Hanyu , Pang Qidan , Wang Yong , Lai Suhe , Chen Hong
Published: September 2022

Background. Colorectal cancer is one of the most common gastrointestinal malignancies globally. Necroptosis has been proved to play a role in the occurrence and development of the tumor, which makes it a new target for molecular therapy. However, the role of necroptosis in colorectal cancer remains unknown yet. Our study aims to build a prognostic signature of necroptosis-related lncRNAs (nrlncRNAs) to predict the outcomes of patients with colorectal cancer and facilitate in anticancer therapy. Method. We obtained RNA-seq and clinical data of colorectal adenocarcinoma from the TCGA database and got prognosis-related nrlncRNAs by univariate regression analysis. Then, we carried out the LASSO regression and multivariate regression analysis to build the prognostic signature, whose predictive ability was tested by the Kaplan-Meier as well as ROC curves and verified by the internal cohort. Moreover, we divided the cohort into 2 groups based on median of risk scores: high- and low-risk groups. By analyzing the difference in the tumor microenvironment, microsatellite instability, and tumor mutation burden between the two groups, we explored the potential chemotherapy and immunotherapy drugs. Results. We screened out 9 nrlncRNAs and built a prognostic signature based on them. With its good prognostic ability, the risk scores can act as an independent prognostic factor for patients with colorectal cancer. The overall survival rate of patients in high-risk group was significantly higher than the low-risk one. Furthermore, risk scores can also give us hints about the tumor microenvironment and facilitate in predicting the response to the CTLA-4 blocker treatment and other chemotherapeutic agents with potential efficacy such as cisplatin and staurosporine. Conclusions. In conclusion, our prognostic signature of necroptosis-related lncRNAs can facilitate in predicting the prognosis and response to the anticancer therapy of colorectal cancer patients.
Formulation and Development of a Water-in-Oil Emulsion-Based Luliconazole Cream: In Vitro Characterization and Analytical Method Validation by RP-HPLC
Authors: Panthi Vijay Kumar , Nepal Utsav
Published: September 2022

Luliconazole (LCZ) is a new antifungal agent containing imidazole moiety which revealed broad-spectrum antifungal activity. The aim of this research was to prepare water-in-oil (w/o) emulsion-based cream formulation of LCZ in addition to the development and validation of an analytical method by reverse-phase high-performance liquid chromatography (RP-HPLC). Cetostearyl alcohol (12.14%), light liquid paraffin (5.00%), white soft paraffin (2.75%), and Tween-80 (1.00%) appeared as the optimized concentration to give better consistency to the cream. Moreover, without adding pH adjusting agents the pH of the optimized formulation (F5) was obtained within the range of human skin pH throughout the stability period. The value of particle size, polydispersity index, and zeta potential was 187.90 ± 2.061 nm, 0.124 ± 0.026, and -10.553 ± 1.349 mV, respectively. In this study, an analytical C18 (4.6 mm × 25 cm), 5 μm column was used for chromatographic separation with a mixture of acetonitrile and water in the proportion of 50 : 50 v/v as the mobile phase at a flow rate of 1.0 mL/min. The calibration curve was obtained linear at 296 nm in the concentration range of 0.08–0.12 mg/mL. Furthermore, the limit of detection (LOD) and limit of quantification (LOQ) were 0.0013 and 0.0042 µg/mL, respectively. In addition, the observed results demonstrated that our developed method was linear (R2 = 0.999), precise (%RSD below than 2.0%), and accurate (mean recovery% = 100.18–100.91). The F5 showed no physical changes until 6th month analysis at room temperature and accelerated conditions. Similarly, the assay obtained 101.99% ± 0.27 and 99.89% ± 0.08 at room temperature and accelerated conditions, respectively. Additionally, all validated parameters were obtained within the acceptable limit as well. These findings conclude that both physically and chemically stable w/o cream formulation of LCZ can be formulated and assessed for their stability by applying the authenticated analytical procedure of RP-HPLC.
Analytical Investigation of Some Dynamical Systems by ZZ Transform with Mittag–Leffler Kernel
Authors: Areshi Mounirah , Naeem Muhammad , Wyal Noorolhuda
Published: September 2022

In this work, ZZ transformation is combined with the Adomian decomposition method to solve the dynamical system of fractional order. The derivative of fractional order is represented in the Atangana–Baleanu derivative. The numerical examples are combined for their approximate‐analytical solution. It is explored using graphs that indicate that the actual and approximation results are close to each other, demonstrating the method’s usefulness. Fractional‐order solutions are the most in line with the dynamics of the targeted problems, and they provide an endless number of options for an optimal mathematical model solution for a particular physical phenomenon. This analytical approach produces a series form solution that is quickly convergent to exact solutions. The acquired results suggest that the novel analytical solution technique is simple to use and very successful at assessing complicated problems that arise in related fields of research and technology.
An Artificial Intelligence-Based Approach to Social Data-Aware Optimization for Enterprise Management
Authors: Zhang Weiwei
Published: September 2022

Enterprise management has always been a hot issue in society. In today’s society, enterprises are no longer individuals cut off from society and no longer have profit as their sole purpose, but need to exist and develop in combination with social information. With the increasing competition among enterprises, the original enterprise management methods can no longer meet the needs of sustainable development of enterprises, nor can they effectively utilize social data. To better understand the daily emotions of corporate workers, we use a multimodal emotion recognition method in this paper. Multimodal emotion recognition refers to the recognition of human emotional states through different modal information such as speech, visual, and text related to human emotional expressions, which has important research significance in the fields of human-computer interaction, artificial intelligence, and emotional computing and has received much attention from researchers. Given the great success of deep learning methods developed in recent years for various tasks, various deep neural networks are now used to learn high-level representations of emotional features for multimodal emotion recognition. The analysis of employee sentiment is complemented by traditional management methods that make full use of social data. In this paper, based on the study of a single enterprise management model, the proposed model contains five substructure modules, starting with feature inputs, extracting features at different levels through three convolutional modules and outputting recognition results through a softmax classifier. The focus is on how to utilize social data, while combining deep learning with traditional enterprise management methods to fill the research gap in this area in academia.
Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis
Authors: Xing Lu , Wu Tao , Yu Li , Zhou Nian , Zhang Zhao , Pu Yunjing , Wu Jinnan , Shu Hong
Published: September 2022

Background. Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. Methods. GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region- (DMR-) genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, a protein-protein interaction (PPI) network was established to analyze the interaction between differentially expressed methylated genes. Moreover, the hub genes of psoriasis were screened by the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), which were further performed single-gene gene set enrichment analysis (GSEA) to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. Finally, the expressions of hub genes in psoriasis lesions and healthy controls were detected by immunohistochemistry (IHC) and quantitative real-time PCR (RT-qPCR). Results. In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which GJB2 was the final identified hub gene for psoriasis, with robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significantly higher in psoriasis samples than those in healthy controls. Additionally, GJB2 may be involved in the development and progression of psoriasis by disrupting the body’s immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database. Conclusion. The abnormal expression of GJB2 might play a critical role in psoriasis development and progression. The genes identified in our study might serve as a diagnostic indicator and therapeutic target in psoriasis.
An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network
Authors: Wu Yuhong , Hu Xiangdong
Published: September 2022

Now, the use of deep learning technology to solve the problems of the low multiclassification task detection accuracy and complex feature engineering existing in traditional intrusion detection technology has become a research hotspot. In all kinds of deep learning, recurrent neural networks (RNN) are very important. The RNN processes 41 feature attributes and maps them to a 122-dimensional high-dimensional feature space. To detect multiclassification tasks, this study proposes an intrusion detection method based on fully connected recurrent neural networks and compares its performance with previous machine learning methods on benchmark datasets. The research results show that the intrusion detection system (IDS) model based on fully connected recurrent neural network is very suitable for classification of intrusion detection. Classification methods, especially in multiclassification tasks, have high detection accuracy, significantly improve the detection performance of detection attacks and DoS attacks, and it provides a new research direction for the future attempts of intrusion detection methods for industrial control systems.
Optimization Method of RFID Reader Antenna Deployment in Obstacle Environment Based on Improved FA
Authors: Hong Tao , Jiang Yixuan , Wang Cui
Published: September 2022

The aim of this study is to solve the problem of RFID reader antenna deployment when an obstacle in the environment or the material itself is an obstacle. The article establishes a new reader antenna constraint model based on the rectangular obstacle RFID antenna deployment optimization environment model and the reader antenna sensing model containing Gaussian distribution noise probability and applies the improved firefly algorithm to find the optimum with coverage rate, interference degree, and load balance function as multiobjective functions. First, to obtain a uniform traversal of the target space, and a cube mapping is applied for chaos initialization. Second, a weighting model is proposed to control the speed of firefly variation and combine the variable step strategy of elite individuals to expand the movement step of elite individuals and reduce the movement step of nonelite individuals to improve population diversity. Finally, to improve the algorithm’s continuous optimality-seeking ability, a Gaussian variation operation strategy based on elite fireflies is introduced to ensure the algorithm’s optimality-seeking throughout the process.