bluuebunny/crossref_metadata_2025 huggingface dataset:
Torsional buckling and postbuckling behavior of stiffened FG-GRCL toroidal shell segments surrounded by elastic foundation
Authors: Doan Cao Van , Hung Vu Tho , Phuong Nguyen Thi , Nam Vu Hoai
Published: September 2022

In this study, a new algorithm for the nonlinear buckling behavior of the stiffened functionally graded graphene-reinforced composite laminated (FG-GRCL) toroidal shell segments in the thermal environment surrounded by the elastic foundation and subjected to torsional load is introduced. The FG-GRCL shell segment is stiffened by the FG-GRCL ring and/or stringer stiffener system. Based on the Donnell shell theory with the Stein and McElman approximation, considering the von Karman geometrical nonlinearity and the anisotropic smeared stiffener technique for FG-GRCL stiffeners, the formulations to investigate the critical buckling load and postbuckling torsional load-deflection curves are determined. A new algorithm is fully developed by adding the effect of circumferential stress into the stress function. The significant effects of temperature, stiffener system, geometrical parameters, and elastic foundation are considered and studied in detail.
Fingero-imbibition phenomenon with magnetic field effect in two-phase flow through porous medium
Authors: Patel M. A. , Desai N. B. , Prajapati D. J.
Published: September 2022

This paper discusses the problem of fingero-imbibition phenomenon with magnetic field effect. The problem arises in two-phase flow through homogeneous porous medium during secondary oil recovery process. The mathematical formulation gives us one-dimensional nonlinear partial differential equation. Homotopy analysis method is adopted to solve the governing equation. The solution is discussed numerically and graphically with the help of Mathematica BVPh package.
DEEP-SEA MINING FOR RENEWABLE TECHNOLOGIES: A CASE STUDY FOR USING BIG DATA
Authors: CRABBE M. JAMES C.
Published: September 2022

Depleting terrestrial deposits and rising demand for metals in this time of climate change are stimulating interest in the deep sea — the area of the ocean below 200[Formula: see text]m — with commercial mining of mineral deposits imminent. Environmental impact assessments, effective regulation and mitigation strategies are needed to limit the impacts of deep-sea mining. This is a potential catastrophe for marine biodiversity, and use of Big Data will be needed to follow mining activities as well as chart impacts on known and unknown marine species and their habitats.
A Bibliometric Analysis of English Literature on Campus Emergency Management
Authors: Wang Yujia , Yu Li , Zhang Qi , Zhang Yan , Liu Mengmeng , Sun Xiaonan , Yin Mei , Wu Yibo
Published: September 2022

Introduction: We adopt the method of bibliometrics to analyze the annual volume of papers and author distribution in the field of campus emergency management in the past 43 years, to evaluate the main population and types of emergency in this study of campus emergency management, and to discuss the existing management methods. Thus, we explore the research hotspot, research gaps and provide reference for future research. Method: We made a statistical analysis of published literature through bibliometrics, searched relevant papers on campus emergency management in PubMed, Embase and Web of Science databases and included 364 valid literature works after screening. The characteristics were analyzed from multiple perspectives. Results: The research on campus emergency management is divided into three stages: “incubation period”, “fluctuating growth period” and “rapid development period”. Studies vary widely from continent to continent. There are many differences in the focus groups of different school types in the literature. The proportion of emergencies is divided into wound, disaster, school violence, illness and bombshell and others, accounting for 34.17%, 16.94%, 16.11%, 12.78%, 4.44% and 15.56%, respectively. Conclusion: Through the statistical analysis of English literature on campus emergency management from 1977 to 2020, it can be seen that the uneven distribution of research areas is very serious, and there is not much attention to special types of schools. The subdivision and management of the population are in continuous improvement. The research on campus emergency management has been continuously carried out and improved, with a promising prospect. Practical applications: This study can help explain the current situation and development trend of international campus emergency management, and provide reference for further research.
Discovery of Novel and Potent Inhibitors Against Mutational Variants of IDH1 Protein for Glioma Therapy: A Fragment-Based Approach
Authors: Murali Poornimaa , Karuppasamy Ramanathan
Published: September 2022

Somatic mutations in the isoenzymes of isocitrate dehydrogenases (IDHs) account for the pathogenesis of various malignancies including gliomas. To date, ivosidenib is the only FDA-approved drug candidate widely used to target mutated isocitrate dehydrogenase 1 (mIDH1). However, the impotence of the existing drug to evade the blood barrier remains an obstacle to harness ivosidenib as therapeutics against glioma. Thus, in this study, we elucidate a fragment-based drug discovery strategy to design novel lead molecules against the mIDH1 protein. Initially, a fragment library was constructed using 27 known mIDH1 inhibitors from the literature. The vast chemical classes of constructed library consisting of 1109 fragments were then used for breeding. A total of 18,000 breed compounds were generated and the resultant compounds were scrutinized based on the breed score ([Formula: see text]) and Tanimoto coefficient ([Formula: see text]). The binding affinity and the energetics of the resultant molecules (2069) were investigated using molecular docking and MM-GBSA calculations. Eventually, the compounds with higher affinity were included in the mutational analysis incorporating the second site mutations namely IDH1S280F and IDH1R119P. The bioavailability analysis and toxicity profiling were carried out for screened hybrid molecules. The pipeline of the integrated in-silico approach identified hybrid 209, hybrid 237 and hybrid 504 as the drug-like candidates against the mutational variants of mIDH1 protein. Interestingly, all three compounds exhibited greater binding affinity and better brain penetrating capability. The machine learning-based anti-cancerous sensitivity prediction tool affirmed the inhibitory effect of the resultant hits against various glioma cell lines. In the end, the structural stability of the screened molecules was examined using the molecular dynamic simulation study for a stipulated time of 100[Formula: see text]ns. Indeed, this evidence speculates that the identified hybrid molecules could serve as important leads for the management of glioma in the near future.
Regional Financial Data Processing Based on Distributed Decoding Technology
Authors: Zhou You
Published: September 2022

Aiming at the problems of low storage efficiency, small storage space, and poor stability of traditional financial data storage systems, a dynamic and secure financial data storage system based on distributed decoding is designed. The system uses a cloud security storage service system to store a large number of financial data obtained from the sensing layer and achieve the reasonable scheduling of financial dynamic cloud data through the constraint software function. In addition, a distributed security computing scheme based on hierarchical coding is proposed, and the influence of different subtask sizes and different computing layers on the scheme is analyzed. The results show that the distributed security computing scheme based on hierarchical coding can significantly reduce the completion time of the overall computing task and enhance the stability of dynamic financial data storage. This system has a good storage performance of financial big data, and its efficiency is good, which provides strong support for the financial cause of our country.
CASP9 As a Prognostic Biomarker and Promising Drug Target Plays a Pivotal Role in Inflammatory Breast Cancer
Authors: Zhang Mingdi , Wu Kejin , Wang Maoli , Bai Fang , Chen Hongliang
Published: September 2022

Background. Inflammatory breast cancer (IBC) is one of the most rare and aggressive subtypes of primary breast cancer (BC). Our study aimed to explore hub genes related to the pathogenesis of IBC, which could be considered as novel molecular biomarkers for IBC diagnosis and prognosis. Material and Methods. Two datasets from gene expression omnibus database (GEO) were selected. Enrichment analysis and protein-protein interaction (PPI) network for the DEGs were performed. We analyzed the prognostic values of hub genes in the Kaplan-Meier Plotter. Connectivity Map (CMap) and Comparative Toxicogenomics Database (CTD) was used to find candidate small molecules capable to reverse the gene status of IBC. Results. 157 DEGs were selected in total. We constructed the PPI network with 154 nodes interconnected by 128 interactions. The KEGG pathway analysis indicated that the DEGs were enriched in apoptosis, pathways in cancer and insulin signaling pathway. PTEN, PSMF1, PSMC6, AURKB, FZR1, CASP9, CASP6, CASP8, BAD, AKR7A2, ZNF24, SSX2IP, SIGLEC1, MS4A4A, and VSIG4 were selected as hub genes based on the high degree of connectivity. Six hub genes (PSMC6, AURKB, CASP9, BAD, ZNF24, and SSX2IP) that were significantly associated with the prognosis of breast cancer. The expression of CASP9 protein was associated with prognosis and immune cells infiltration of breast cancer. CASP9- naringenin (NGE) is expected to be the most promising candidate gene-compound interaction for the treatment of IBC. Conclusion. Taken together, CASP9 can be used as a prognostic biomarker and a novel therapeutic target in IBC.
Automatic Distributed Control Method for Indoor Low-Voltage Electricity Based on Modal Symmetry Algorithm
Authors: Lian Han
Published: September 2022

The traditional control method has a low margin of indoor voltage stability, which can easily lead to voltage collapse, and the control effect is not ideal. An automatic distributed control method for indoor low-voltage electricity system based on modal symmetry algorithm is proposed. In order to improve the voltage stability margin, the decoupling between the indoor low-voltage electricity characteristics and regions was analyzed. Based on the analysis results, the decoupling between regions and the robustness to faults of the distributed zone scheme were considered comprehensively, and the modularity and the optimal zoning scheme were screened. Based on the principle of modal symmetry, the original data of the distributed control matrix of low-voltage electricity was obtained. Mean standardization method was used to standardize the matrix, so as to realize the distributed control of indoor low-voltage electricity. The experimental results show that the proposed method improves the margin of voltage stability, overcomes the drawbacks of traditional methods, and reduces the probability of voltage accidents. It shows that this method has higher flexibility, higher reliability, and higher application value as well as improving the automatic control level of voltage.
[Retracted] Visual Communication Design Model for New Media and Public Health Environment and New Communication Mode
Authors: Wang Ke , Kuang Caiyuan
Published: September 2022

When we examine the development history of visual transmission design, we can see that media technology application and visual transmission design innovation are frequently intertwined. The relevant work outcomes attained during the process of art design practice typically exhibit the traits of dynamic and dynamic performance with a clear degree of expression, against the backdrop of the practical advancement and widespread use of developing media technology. In light of the influence of new media art, it is essential to understand the development trend of art design in order to perform well in visual transmission design. The audience will be given access to more varied and richer visual transmission information thanks to the wise deployment of multimedia techniques. This work develops a three‐dimensional picture reconstruction algorithm based on the visual transmission phenomenon. The relative error, matching degree, and signal‐to‐noise ratio were chosen as evaluation indices. Three sets of tests were conducted to determine how noiseless image, noisy image, and various sampling frequencies affected the quality of the reconstructed image. The testing results demonstrate that the three‐dimensional picture rebuilding system has a 22.34% higher antinoise ability and has a significantly superior overall effect than the conventional three‐dimensional image rebuilding system. It offers greater practical application value and does a decent job of keeping image details and edges.
Gastric Mucosa Pathology in Rats with Precancerous Lesions of Gastric Cancer with Spleen Deficiency and Blood Stasis
Authors: Li Qiu-yue , Yang Peng-hui , Huang Xin , Li Xin-long , Luo Min-yi , Xiao Bi-juan , Zhao Zi-ming , Li Si-yi , Pan Hua-feng
Published: September 2022

Objective. This research aimed at better understanding the histopathological development of precancerous lesions of gastric cancer (PLGC) and organelle ultrastructure changes. Methods. Sprague-Dawley rats were randomly assigned to the model and control groups. Model rats drank N-methyl-N′-nitro-N-nitrosoguanidine solution, while control rats drank pure water ad libitum. At 1, 3, 5, 6, and 8 months after the start of feeding, eight rats were randomly chosen from each group, and gastric mucosa tissues were removed for histopathological analysis. H&E staining was applied to analyze the pathological histological structure of the rat gastric mucosa via a light microscope, and the ultrastructural changes were observed via a transmission electron microscope. Results. Gastric mucosal pathologies of model rats such as mucosal atrophy, intestinal metaplasia, inflammatory lesions, and even intraepithelial neoplasia deteriorated over time. The endoplasmic reticulum gap widened, the mitochondrial endothelial cristae were disrupted, the nuclear membrane thickened, and chromatin condensed with heterotypic alterations in the main and parietal cells. Additionally, endothelial cell enlargement and thickening of the microvascular intima were seen. Conclusion. Our research showed that the PLGC progression of rats is correlated with the pathological alteration axis of “normal gastric mucosa-gastric mucosa inflammatory changes-intestinal metaplasia with mild dysplasia-moderate to severe dysplasia.” Ultrastructure analysis of model rats is compatible with the structural changes in the gastric mucosa with spleen deficiency and blood stasis. The pathological evolutionary axis and ultrastructural analysis are helpful for evaluating potential novel herbal therapies for PLGC.
Comparison Between Real-Time Ultrasound-Guided Percutaneous Tracheostomy and Surgical Tracheostomy in Critically Ill Patients
Authors: Kang Hyun Tag , Kim Shin Young , Lee Min Ki , Lee Seung Won , Baek Aerin , Park Ki Nam
Published: September 2022

Background. Ultrasound-guided percutaneous dilatational tracheostomy (US-PDT) has been adapted for use in intensive care units (ICU). US-PDT is comparable to bronchoscopy-assisted tracheostomy. However, compared to surgical tracheostomy (ST), its safety and effectiveness have not been well studied. Objectives. To determine the efficacy and safety of US-PDT compared to ST. Materials and Methods. A total of 90 patients who underwent US-PDT (n = 36) or ST (n = 54) between July 2019 and September 2020 were enrolled. US-PDT was performed in the ICU without a surgical assistant or bronchoscope. Data were collected retrospectively and analyzed regarding clinical characteristics, procedure times and details, complications, and mortality rate. Results. The success rate of US-PDT was 97.4% and the procedure time was shorter than ST (5.2 ± 3.1 vs. 10.5 ± 5.0 min). There were no significant differences in clinical characteristics and procedure details. There was no procedure-related mortality in either of the groups. Conclusions. US-PDT is time-efficient and as safe as ST. Based on our results, US-PDT may be considered a potential alternative to ST in high-risk patients and in those who cannot be transported.
Multiple Local-Edge-Cloud Collaboration Strategies in Industrial Internet of Things: A Hybrid Genetic-Based Approach
Authors: Liu Xiaokai , Xu Fangmin , Xiao Ye , Zhou Xiaoming , Li Zhao , Zhao Chenglin , Zhang Min
Published: September 2022

To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices.
A Multigranularity Text Driven Named Entity Recognition CGAN Model for Traditional Chinese Medicine Literatures
Authors: Ma Yuekun , Liu Yun , Zhang Dezheng , Zhang Jiye , Liu He , Xie Yonghong
Published: September 2022

Recognition of Traditional Chinese Medicine (TCM) entities from different types of literature is challenging research, which is the foundation for extracting a large amount of TCM knowledge existing in unstructured texts into structured formats. The lack of large-scale annotated data makes unsatisfactory application of conventional deep learning models in TCM text knowledge extraction. Some other unsupervised methods rely on other auxiliary data, such as domain dictionaries. We propose a multigranularity text-driven NER model based on Conditional Generation Adversarial Network (MT-CGAN) to implement TCM NER with small-scale annotated corpus. In the model, a multigranularity text features encoder (MTFE) is designed to extract rich semantic and grammatical information from multiple dimensions of TCM texts. By differentiating the conditional constraints of the generator and discriminator of MT-CGAN, the synchronization between the generated tag labs and the named entities is guaranteed. Furthermore, seeds of different TCM text types are introduced into our model to improve the precision of NER. We compare our method with other baseline methods to illustrate the effectiveness of our method on 4 kinds of gold-standard datasets. The experiment results show that the standard precision, recall, and F1 score of our method are higher than the state-of-the-art methods by 0.24∼8.97%, 0.89∼12.74%, and 0.01∼10.84%. MT-CGAN is able to extract entities from different types of TCM literature effectively. Our experimental results indicate that the proposed approach has a clear advantage in processing TCM texts with more entity types, higher sparsity, less regular features, and a small-scale corpus.
Visual Communication-Based Virtual Reality Design of Imaging Information Collection and Display System
Authors: Qi Yichen , Sun Tong , Li Yan
Published: September 2022

Imaging image acquisition and display has always been an important application field of visual communication art. A good imaging acquisition and display system can give the audience an excellent visual experience. However, conventional imaging image acquisition equipment is too bulky and expensive. At the same time, the existing imaging acquisition technology is generally only suitable for two-dimensional plane design. It is difficult to construct the three-dimensional and realistic sense of actual objects, which makes the development of the entire imaging system slow. Existing imaging acquisition technologies have difficulty keeping pace with the technological age. In this paper, according to the actual needs, a three-dimensional imaging technology was proposed to improve the efficiency of imaging image acquisition. And the principle of imaging information display was used to enhance the visual communication effect. Then, at the algorithm level, virtual reality technology was combined with convolutional neural network-related algorithms to improve the overall accuracy of the algorithm and control the error well. The experimental results showed that the optimized VR technology leads the whole stage in terms of mean square error. Among them, after testing 400 samples in the two experiments, the error performance is controlled below 0.32, and the best error control performance is 0.07; the unoptimized virtual reality technology error control is not ideal. The minimum error failed to break below 0.2, and the optimized algorithm had a high accuracy of 99.3%, which greatly improved the feasibility of the imaging information acquisition and display system.
Novel Drug Design for Treatment of COVID-19: A Systematic Review of Preclinical Studies
Authors: Mousavi Sarah , Zare Shima , Mirzaei Mahmoud , Feizi Awat
Published: September 2022

Background. Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in discovering potential therapeutic agents for this disease. In this regard, we conducted a systematic review through an overview of drug development (in silico, in vitro, and in vivo) for treating COVID-19. Methods. A systematic search was carried out in major databases including PubMed, Web of Science, Scopus, EMBASE, and Google Scholar from December 2019 to March 2021. A combination of the following terms was used: coronavirus, COVID-19, SARS-CoV-2, drug design, drug development, In silico, In vitro, and In vivo. A narrative synthesis was performed as a qualitative method for the data synthesis of each outcome measure. Results. A total of 2168 articles were identified through searching databases. Finally, 315 studies (266 in silico, 34 in vitro, and 15 in vivo) were included. In studies with in silico approach, 98 article study repurposed drug and 91 studies evaluated herbal medicine on COVID-19. Among 260 drugs repurposed by the computational method, the best results were observed with saquinavir (n = 9), ritonavir (n = 8), and lopinavir (n = 6). Main protease (n = 154) following spike glycoprotein (n = 62) and other nonstructural protein of virus (n = 45) was among the most studied targets. Doxycycline, chlorpromazine, azithromycin, heparin, bepridil, and glycyrrhizic acid showed both in silico and in vitro inhibitory effects against SARS-CoV-2. Conclusion. The preclinical studies of novel drug design for COVID-19 focused on main protease and spike glycoprotein as targets for antiviral development. From evaluated structures, saquinavir, ritonavir, eucalyptus, Tinospora cordifolia, aloe, green tea, curcumin, pyrazole, and triazole derivatives in in silico studies and doxycycline, chlorpromazine, and heparin from in vitro and human monoclonal antibodies from in vivo studies showed promised results regarding efficacy. It seems that due to the nature of COVID-19 disease, finding some drugs with multitarget antiviral actions and anti-inflammatory potential is valuable and some herbal medicines have this potential.
The Application of Traditional Chinese Woodcut Printmaking Language in Digital Painting Based on Intelligent Computing
Authors: Chen Changhuan
Published: September 2022

With the passage of time, information technology has been implicitly embedded in people’s lives, bringing an important impact on artists’ creations. Modern painting technology is advanced, using electronic products, digital devices, electronic hand-painting tools, and other equipment to simulate the real texture of brush strokes to paint the style and effect the artist seeks. The integration of woodcut prints into digital painting has enriched the form of digital painting, and one of the characteristics of digital illustration is its fast dissemination, which is more powerful to promote the charm of traditional art. In the new era, with new technology, we consider whether we can combine the digital painting style, expression, woodcut language, and cultural connotation of ancient Chinese woodcut prints with digital technology and use new methods and forms to speak about the unique and essential attributes of ancient Chinese woodcut prints to be innovated and continued. The emotional motivation and the implicit, obscure connotations of the art of painting are not as far apart as they seem, although they are more like logical computerized counterparts. The continued development of artificial intelligence has made it possible for computers to create paintings independently. This paper explores a process flow model for digital painting based on intelligent computing and effectively incorporates the language of traditional Chinese woodcut printmaking while looking at future trends in this field.
Heat Index Based Optimisation of Primary Process Parameters in Friction Stir Welding on Light Weight Materials
Authors: Joseph Leon Stephen Leon , Manikandan N. , Santhanakrishnan R. , Al-Khafaji Mohsin O. , Mahmood Salman Hayder , Anandaram Harishchander , Malkiya Rasalin Prince R. , Karthick L. , Rajesh Sharma R.
Published: September 2022

In friction stir welding, tool shoulder diameter and its rotational speed are the major influencing parameters than others. A simple novel correlation is proposed to select the optimum range of tool shoulder diameter with respect to the chosen rotational speed and vice versa. The conditions to apply derived correlation were defined through process heat index number as the joint efficiency in the friction stir welding depends on the effective heat supply to the volume of material deformed in the stir zone. Weld speed is the key parameter through which generated heat can be regulated towards optimum heat supply to attain defect-free weld in the stir zone. Effective heat input also has obvious effect on grain growth and corresponding property eradication in the heat affected zone. The experimental study was carried out on AA2024-T3 plates to understand the effect of process heat index on the prescribed optimum range of tool shoulder and rotational speed defined in the correlation. Eventually, a novel relationship was attained between the first order process influencing parameters to deliver maximum joint efficiency.
Construction and Application of Music Style Intelligent Learning System Based on Situational Awareness
Authors: Chen Yunfei
Published: September 2022

Contextual representation recommendation directly uses contextual prefiltering technology when processing user contextual data, which is not the integration of context and model in the true sense. To this end, this paper proposes a context-aware recommendation model based on probability matrix factorization. We design a music genre style recognition and generation network. In this network, all the sub-networks of music genres share the explanation layer, which can greatly reduce the learning of model parameters and improve the learning efficiency. Each music genre sub-network analyzes music of different genres, realizing the effect of multitasking simultaneous processing. In this paper, a music style recognition method using a combination of independent recurrent neural network and scattering transform is proposed. The relevant characteristics of traditional audio processing methods are analyzed, and their suitable application scenarios and inapplicability in this task scenario are expounded. Starting from the principle of scattering transform, the superiority and rationality of using scattering transform in this task are explained. This paper proposes a music style recognition method combining two strategies of scattering transform and independent recurrent neural network. In the case that the incremental data set is all labeled, this paper introduces the solution of the convex hull vector, which reduces the training time of the initial sample. According to the error push strategy, an incremental learning algorithm based on convex hull vector and error push strategy is proposed, which can effectively filter historical useful information and at the same time eliminate useless information in new samples. Experiments show that this method improves the accuracy of music style recognition to a certain extent. Music style recognition based on independent recurrent neural network can achieve better performance.
[Retracted] Evaluation of College Students’ IPE Environment Based on User Rating Sparsity and Big Data Technology
Authors: Zou Nan , Wang Enfu
Published: September 2022

Big data improves opportunities for enhancing and improving university students’ IPE. In order to improve the accessibility of IPE for university students, this study integrates big‐data techniques into the IPE (Ideological and Political Education) model of university students and builds the IPE platform. This study presents the idea of user rating sparsity and employs a two‐step training strategy to address the issues of user cold start and sparse data in light of the drawbacks of conventional methods. This algorithm produces a very small data structure, which saves a lot of storage space. This study also uses hybrid recommendation technology, which effectively enables platform users to select customized update resources based on their interest information. According to test results, this method’s suggestion accuracy can reach 95.69%, and it has a high user rating. This demonstrates that the method is reliable and accomplishes the desired result. This paper fully utilizes mega data to improve the accessibility of IPE for university students.
Existence and Uniqueness of Solutions for a Fractional Hybrid System with Nonseparated Integral Boundary Hybrid Conditions
Authors: Hannabou M. , Bouaouid M. , Hilal K.
Published: September 2022

In this paper, we are going to investigate the existence and uniqueness of solutions of a coupled system of nonlinear fractional hybrid equations with nonseparated type integral boundary hybrid conditions. We are going to use Banach’s and Leray-Schauder alternative fixed point theorems to obtain the main results. Lastly, we are giving two examples to show the effectiveness of the main results.
Productivity Prediction and Simulation Verification of Fishbone Multilateral Wells
Authors: Jing Wen , Xiao Liu , Haixia Hu , Wei Luo
Published: September 2022

As fishbone multilateral wells are increasingly used in the development of oil and gas fields, the need for the optimal design of multilateral well parameters has become increasingly urgent. Although the establishment of multilateral well productivity models has taken a step forward due to recently contributed research results, there are still few studies on model verification; that is, these models lack verification, and their reliability is uncertain, which increases the difficulty of their promotion and application in practice. Therefore, based on a more comprehensive horizontal well productivity prediction model, a fishbone multilateral well productivity prediction model was established in this work. Taking an oil field in the Middle East as an example, multilateral well productivity prediction and parameters sensitivity analysis were carried out, and the results were compared with the simulation results determined by using CMG digital simulation software. The established model was verified in terms of branch length, branch angle, and branch output contribution. The conclusions reached by these two methods were consistent, which verified that the established model was reliable.
[Retracted] Investigating the Lung Adenocarcinoma Stem Cell Biomarker Expressions Using Machine Learning Approaches
Authors: Bhuvaneswari M. S. , Priyadharsini S. , Balaganesh N. , Theenathayalan R. , Hailu Tegegne Ayalew
Published: September 2022

The objective of the study is to look at the activation of stem cell‐related markers in lung adenocarcinoma. Utilizing an unsupervised machine learning approach centered on the mRNA expression of pluripotent stem cells as well as its subsequent developed progeny, the mRNA stemness index of further around 500 LUAD patients from The Cancer Genome Atlas dataset was generated. In LUADs, mRNAsi had first been investigated using differential variations, survivability analyses, medical phases, and sexuality. A computational approach is used for identifying cell clusters utilizing fuzzy clustering. There at transcriptional as well as protein stages, the interactions between the genetic markers were investigated. The functionality and processes of the important genes were annotated using expression values. The degree of gene expression related to the clinical symptoms and the likelihood of surviving have also been confirmed. In cancer patients, the mRNAsi genes were highly elevated. In particular, the mRNAsi score rises with advanced trials and varies markedly by sexuality. Within several years, reduced mRNAsi categories will have superior overall survivability in large LUADs. Individuals with chronic LUAD had greater mRNAsi and had reduced average survivability. The important genes and the distinguished categories have been chosen based on their mRNAsi connections. Some of the major genes related to cell proliferating Gene Ontology concepts were found enriched out from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) process. Specific genes were found to be linked to CSC features. Their activation grew in lockstep with the progression of LUAD’s pathology, so these markers appeared amplified in pan‐cancers. These important markers were discovered to have substantial connections as a group, suggesting that they could be exploited as drug applications in the therapy of LUAD by suppressing stemness traits.
Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve
Authors: Zhao Yuqi
Published: September 2022

Interactive art design (IAD) is an organic integration of art and technology. From the perspective of the development of AI machines from ancient times to the present, it has gone through the stages of command interface, graphical interface, and multimedia interface. The development of interactive art has been around for some years. As an art form, it not only brings optimization and enjoyment to people’s quality of life but also meets the needs of human-computer interaction, improves the efficiency of art design, and realizes human-human interaction. The purpose is to bring different feelings and experiences to the works and people’s psychology. In particular, the application of AI in IAD has not only brought great changes to designers. It also derives the interaction of behavioral limbs, which brings a greater experience and interaction to the audience, thereby creating a better interactive effect. Therefore, this paper completes the following work: (1) the research progress of AI in IAD at home and abroad is introduced. (2) The combination of AI and IAD is proposed, and the basic principle of RBF neural network and intelligent optimization algorithm are introduced, and the evaluation index of IAD is constructed. (3) Using the constructed dataset to test two intelligent optimization algorithms, the results show that the PSO-RBF model is more excellent in evaluating the quality of IAD. The trained model is used for experiments, and the output of the model is compared with the expert evaluation results, and the error is very small. Comparing the quality indicators of IAD before and after the integration of AI, the results show that AI has an excellent improvement effect on IAD.
Equilibrium Isotherms, Kinetics, and Thermodynamic Mechanisms of a Novel Polyacrylamide-Strychnos potatorum Seed-Derived Activated Carbon Composite for Aqueous Hardness Removal
Authors: Mathota Arachchige Yohan L. N. , Fernando T. D.
Published: September 2022

Hardness in water is responsible for both residential and industrial problems. Moreover, drinking hard water is suspected as the main cause of chronic kidney disease of unknown etiology (CKDu) in Sri Lanka. The major constituents that are responsible for water hardness are calcium and magnesium ions. In this study, a composite was synthesized using activated carbon of Strychnos potatorum seeds (ACSP) and acrylamide to remove hardness in drinking water. The synthesized composite was characterized using Fourier transform infrared-attenuated total reflection (FTIR-ATR) spectroscopy and scanning electron microscope (SEM). According to this study, the process of removal of hardness depends on the contact time, adsorbent dosage, initial contents, and pH of the solution. The adsorption data were well fitted to the Freundlich isotherm and the pseudo-second-order kinetic models. Furthermore, environmental samples collected from Anuradhapura, Sri Lanka, which is well known for water with high hardness, were treated with an adsorbent, and hardness was reduced effectively. Moreover, the adsorption appeared to be spontaneous in nature. Finally, it can be concluded that this adsorbent can be used as an effective hardness-removing agent.
Application of Facies Controlled Prestack Inversion Technology in Prediction of Deep Tight Gas in Continental Faulted Basin
Authors: Guan Xiaowei , Meng Qian , Jiang Chuanjin , Chen Xuehai , She Rui , Sun Lidong
Published: September 2022

It is difficult to predict tight sandstone gas in continental rift basin because of complex deep structure, fast transformation of sedimentary facies, large lateral variation of reservoir thickness, and tight and strong heterogeneity. Based on the petrophysical analysis of logging data, this paper improved the traditional low-frequency establishment method of prestack synchronous inversion which is under the constraints of fine isochronal sequence stratigraphy framework. The prestack AVO has attributed as covariate, and Bayes kriging method has applied to establish low-frequency model. Then, the relationship between density and P-wave and S-wave impedance has been introduced. The results show that the stability of density parameters has been improved, and the facies controlled prestack inversion with drilling verification has been realized which the number of layers with thickness error less than 20% accounts for 75.2%. Exploration practice shows that the facies controlled prestack inversion technology has important reference significance for other deep continental fault basins.
Identification of MMP9 as a Novel Biomarker to Mitochondrial Metabolism Disorder and Oxidative Stress in Calcific Aortic Valve Stenosis
Authors: Liu Cong , Liu Ruixue , Cao Zhezhe , Guo Qiao , Huang He , Liu Liangming , Xiao Yingbin , Duan Chenyang , Ma Ruiyan
Published: September 2022

Calcific aortic valve stenosis (CAVS) is the most common heart valve disorder among humans. To date, no effective method has been identified to prevent this disease. Herein, we aimed to identify novel diagnostic and mitochondria-related biomarkers of CAVS, based on two machine learning algorithms. We further explored their association with infiltrating immune cells and studied their potential function in CAVS. The GSE12644, GSE51472, and GSE83453 expression profiles were downloaded from the Gene Expression Omnibus (GEO) repository. The GSE12644 and GSE51472 datasets were integrated to identify differentially expressed genes (DEGs). GSE12644 contains 10 normal and 10 CAVS samples, whereas GSE51472 contains 5 normal and 10 CAVS samples. GO and KEGG assays of DEGs were conducted, and the correlation between matrix metalloproteinase 9 (MMP9) expression and immune cell infiltration was explored, using CIBERSORT. The LASSO regression model and SVM-RFE analysis were used to identify diagnostic genes. The expression of MMP9 in CAVS and non-CAVS samples was measured using RT-PCR, western blotting and immunohistochemistry. A series of functional experiments were performed to explore the potential role of MMP9 in mitochondrial metabolism and oxidative stress during CAVS progression. Twenty-two DEGs were identified, of which six genes (SCG2, PPBP, TREM1, CCL19, WIF1, and MMP9) were ultimately distinguished as diagnostic genes in CAVS. Of these, MMP9 was indicated as a mitochondria-related gene, the expression and diagnostic value of which were further confirmed in the GSE83453 dataset. Correlation analysis revealed a positive correlation between MMP9 and infiltrating immune cells. In our cohort, MMP9 expression was distinctly increased in CAVS samples, and its inhibition attenuated the calcification of valve interstitial cells (VICs) by suppressing mitochondrial damage and oxidative stress. Taken together, our findings suggest MMP9 as a novel mitochondrial dysfunction biomarker and therapeutic target for CAVS.
Probiotics Bring New Hope for Atherosclerosis Prevention and Treatment
Authors: Zhai Taiyu , Wang Pingping , Hu Xiumei , Zheng Lei
Published: September 2022

Cardiovascular disease is the leading cause of human mortality and morbidity worldwide. Atherosclerosis (AS) is the underlying pathological responsible in most acute and severe cardiovascular diseases including myocardial infarction and stroke. However, current drugs applied to the treatment of AS are not clinically effective, and there is a large residual risk of cardiovascular disease and multiple side effects. Increasing evidence supports a close relationship between microorganisms and the incidence of AS. Recent data have shown that probiotics can improve multiple key factors involved in the development and progression of AS, including cholesterol metabolism imbalance, endothelial dysfunction, proinflammatory factor production, macrophage polarization, intestinal flora disturbance, and infection with pathogenic microorganisms, and therefore probiotics have attracted great interest as a novel potential “medicine”. This review is aimed at summarizing the effects of probiotics on various influencing factors, and providing valuable insights in the search for early prevention and potential therapeutic strategies for AS.
Tiny Spherical Robot with a Magnetic Field-Based Interference Detection and Prevention Framework
Authors: Refaai Mohamad Reda A. , Meenatchi S. , Ramesh B. , Rashid N. I. Haroon , Soni N. B. , Ganesan Manikandan
Published: September 2022

Spherical robots are a recent technique and have attracted attention due to their capacity to move at high speeds while maintaining great locomotion efficiency. Many research studies have been undertaken on spherical robots’ driving mechanisms, motion planning, and trajectory tracking systems, but only a few studies have been completed on their obstacle avoidance capacity. Its interfering prevention method was “hit and run” due to the existence of a sealed outer shell. This is convenient because of the spherical robots’ unique shape. It might cause major problems when the robots are light and small in size. A high-speed collision with a hard surface might harm the robot or the camera in portable spherical robots with onboard cameras. In this article, a magnetic field-based interference detection and prevention system for a tiny spherical robot has been established. The proximity sensor uses a passive magnetic field to detect ferromagnetic barriers by causing the magnetic field to be perturbed. It makes use of a passive magnetic field to keep the system small and power-effective. Because the suggested system can sense not only the existence of a ferromagnetic barrier but also its approaching direction, an intelligent avoidance behaviour may be developed by combining the detection information with the trajectory tracking technique. It amplifies the disturbance effectively and hence increases the detection performance. To improve obstacle detection performance, design optimization is carried out and specific avoidance techniques are designed.
Research on Digital Media Art Film and Television Special Effects Technology Based on Virtual and Reality Algorithm
Authors: Sun Lin
Published: September 2022

Art special effects, as a kind of new media art form, bring different visual impacts to viewers in film and television animation. This study discusses the application of digital media art film and television special effects’ technology through virtual and realistic algorithms. The wide application of digital media art film and television special effects’ technology has achieved the purpose of saving production time and cost for the creation of film and television dramas. Through the comprehensive analysis and research on art creation, image impact, film viewing perception, and natural interactive emotion under different technological environments, and the analysis of the comparison results, it can be seen that digital media art film and television special effects’ technology has a far-reaching impact on film and television animation, can better carry out the sustainable development of film and television field, also promote the sustainable development of science and technology to a certain extent, promote the modern development of digital media art design, support society's continuous development and progress, and better promote the all-round integration of digital media art and film and television creation.
Real-Time Leak Detection in High Frequency Hydraulic Cylinder Based on Intelligent Control
Authors: Na Qi , Feng Guosheng , Tian Tixian
Published: September 2022

Most of the existing hydraulic cylinder internal leakage detection methods are laboratory testing methods, mainly the pressure-holding method, measurement of hydraulic cylinder settlement method, and measuring cup measurement method. The internal leakage of the hydraulic cylinder affects the damping characteristics of the control system. Therefore, real-time internal leakage detection plays an important role in the control characteristics. This paper first proposes a wavelet analysis-based extraction of hydraulic cylinder internal leakage fault eigenvalues for analysis, that is, data processing. A convolutional neural network-based detection method is also proposed, in which the pressure signal of a chamber of a hydraulic cylinder is first obtained through simulation under four operating conditions: no leakage, small leakage, medium leakage, and large leakage. Compared to traditional modelling methods, the method overcomes the difficulties in modelling nonlinear hydraulic systems, requires only pressure signal acquisition, is simple and reliable, and is compared with traditional BP neural networks to demonstrate its superiority.