bluuebunny/crossref_metadata_2025 huggingface dataset:
Syndecan-1 in liver pathophysiology
Authors: Reszegi Andrea , Tátrai Péter , Regős Eszter , Kovalszky Ilona , Baghy Kornélia
Published: June 2022

Syndecan-1 (SDC-1) is a heparan sulfate (HS)/chondroitin sulfate proteoglycan (PG) of the cell surface and the extracellular matrix (ECM), which regulates a broad spectrum of physiological and pathological processes such as cell proliferation, migration, inflammation, matrix remodeling, wound healing, and tumorigenesis. Syndecan-1 represents the major PG of the liver, expressed by hepatocytes and cholangiocytes, and its elevated expression is a characteristic feature of liver diseases. The highest syndecan-1 expression is found in liver cirrhosis and in hepatocellular carcinoma (HCC) developed in cirrhotic livers. In addition, as being a hepatitis C receptor, hepatitis C virus (HCV)-infected livers produce extremely large amounts of syndecan-1. The serum levels of the cleaved (shedded) extracellular domain have clinical significance, as their increased concentration reflects on poor prognosis in cirrhosis as well as in cancer. In vivo experiments confirmed that syndecan-1 protects against early stages of fibrogenesis mainly by enhanced clearance of transforming growth factor β1 (TGFβ1) and thrombospondin-1 (THBS1) via circulation, and against hepatocarcinogenesis by interfering with several signaling pathways and enhancing cell cycle blockade. In addition, syndecan-1 is capable to hinder lipid metabolism and ribosomal biogenesis in induced cancer models. These observations together with its participation in the uptake of viruses (e.g., HCV and SARS-CoV-2) indicate that syndecan-1 is a central player in liver pathologies.
Protein phosphatase 1 regulates reactive oxygen species-dependent degradation of histone deacetylase 5 by intermittent hypoxia
Authors: Wang Ning , Prabhakar Nanduri R. , Nanduri Jayasri
Published: June 2022

We recently reported pheochromocytoma 12 (PC12) cells and rats subjected to intermittent hypoxia (IH), a hallmark manifestation of obstructive sleep apnea (OSA), exhibit reduced histone deacetylase activity and HDAC5 protein. Our study further suggested that posttranslational modifications rather than transcriptional mechanism(s) mediate IH-induced HDAC5 degradation. These observations prompted our current study to investigate the mechanism(s) underlying HDAC5 degradation by IH in PC12 cell cultures. IH-induced HDAC5 degradation was blocked by an antioxidant, and reactive oxygen species (ROS) mimetics decreased HDAC5 protein, suggesting that ROS mediates HDAC5 degradation by IH. NADPH oxidases (NOX) 2 and 4 were identified as sources of ROS that mediate the effects of IH. HDAC5 degradation during IH was associated with dephosphorylation of HDAC5 at serine259, and this response was blocked by a NOX inhibitor, suggesting that ROS-dependent dephosphorylation mediates HDAC5 degradation. IH-induced dephosphorylation of HDCA5 was inhibited by calyculin A, an inhibitor of protein phosphatase (PP)-1 and -2, or by the overexpression of nuclear inhibitor of PP1 (NIPP1). HDAC5 dephosphorylation by IH lead to augmented hypoxia-inducible factor (HIF)-1α protein and an increase in its transcriptional activity. These data suggest that PP1-dependent dephosphorylation of S259 destabilizes HDAC5 protein in response to IH, resulting in HIF-1α stabilization and transcriptional activity. Our findings highlight hither to unexplored role of protein phosphatases, especially PP1 in regulating HDAC5 protein, which is an upstream activator of HIF-1 signaling by IH.
Ubiquitin E3 ligase Atrogin-1 protein is regulated via the rapamycin-sensitive mTOR-S6K1 signaling pathway in C2C12 muscle cells
Authors: Nishimura Yusuke , Chunthorng-Orn Jitpisute , Lord Samuel , Musa Ibrahim , Dawson Peter , Holm Lars , Lai Yu-Chiang
Published: June 2022

Atrogin-1 and Muscle-specific RING finger protein 1 (MuRF1) are highly expressed in multiple conditions of skeletal muscle atrophy. The phosphoinositide 3-kinase (PI3K)/Akt/forkhead box (FoxO) signaling pathway is well known to regulate Atrogin-1 and MuRF1 gene expressions. However, Akt activation also activates the mechanistic target of rapamycin complex 1 (mTORC1), which induces skeletal muscle hypertrophy. Whether mTORC1-dependent signaling has a role in regulating Atrogin-1 and/or MuRF1 gene and protein expression is currently unclear. In this study, we showed that activation of insulin-mediated Akt signaling suppresses both Atrogin-1 and MuRF1 protein contents and that inhibition of Akt increases both Atrogin-1 and MuRF1 protein contents in C2C12 myotubes. Interestingly, inhibition of mTORC1 with a specific mTORC1 inhibitor, rapamycin, increased Atrogin-1, but not MuRF1, protein content. Furthermore, activation of AMP-activated protein kinase (AMPK), a negative regulator of the mTORC1 signaling pathway, also showed distinct time-dependent changes between Atrogin-1 and MuRF1 protein contents, suggesting differential regulatory mechanisms between Atrogin-1 and MuRF1 protein content. To further explore the downstream of mTORC1 signaling, we employed a specific S6K1 inhibitor, PF-4708671. We found that Atrogin-1 protein content was dose-dependently increased with PF-4708671 treatment, whereas MuRF1 protein content was decreased at 50 μM of PF-4708671 treatment. However, MuRF1 protein content was unexpectedly increased by PF-4708671 treatment for a longer period. Overall, our results indicate that Atrogin-1 and MuRF1 protein contents are regulated by different mechanisms, the downstream of Akt, and that Atrogin-1 protein content can be regulated by the rapamycin-sensitive mTOR-S6K1-dependent signaling pathway.
Histamine/H1 receptor signaling in the parafacial region increases activity of chemosensitive neurons and respiratory activity in rats
Authors: Sobrinho C. R. , Milla B. M. , Soto-Perez J. , Moreira T. S. , Mulkey D. K.
Published: June 2022

Histamine/H1 receptor signaling activates retrotrapezoid (RTN) neurons under control conditions and to a lesser extent in the presence of serotonin. These results suggest that RTN neurons have a limited capacity to respond to simultaneous activation of multiple Gq-coupled receptors.
Greater neuromuscular fatigue following low-load blood flow restriction than non-blood flow restriction resistance exercise among recreationally active men
Authors: Hill Ethan C. , Rivera Paola M. , Proppe Chris E. , Gonzalez Rojas David H. , Wizenberg Aaron M. , Keller Joshua L.
Published: June 2022

The application of blood flow restriction induces greater acute neuromuscular fatigue relative to nonrestricted conditions. Resistance exercise with blood flow restriction elicited a greater reduction in twitch responses. These neuromuscular differences might explain the more favorable adaptations achieved with blood flow restriction that are likely a function of metabolic stress and subsequent changes in efferent neural drive.
What Motivates Tourists’ Responsible Behavior? An Investigation Based on the Extensive Socialized Model of UTAUT
Authors: Lu Yuyang , Colak Alaaddin , Zhang Jinxin
Published: June 2022

Motivating tourists’ responsible behaviors during their trip has emerged as an essential yet insufficient investigated topic in sustainable tourism research. Ration-based and socialization-based motivators have to be integrated to address the lack of a holistic antecedent framework of responsible tourism behavior. Thus, this study extended the unified theory of acceptance and use of technology (UTAUT) into responsible tourism behavior learning and combined the theory of social influence to establish an extensive socialized model of UTAUT to explain tourists’ adoption of responsible behavior behaviors. This model includes three tourism elements—sustainable benefits, sustainable facility accessibility, social interaction engagement as antecedents, and two types of mediators: ration-based mediators of performance expectation and effort expectation and socialization-based mediators of informative influence and normative influence. 491 Chinese tourists were surveyed to confirm this model. It is found that all four mediators explain tourists’ responsible behaviors. Moreover, sustainable benefits positively influence tourists’ performance and effort expectation and social interaction engagement positively influences informative and normative influences, while sustainable facility accessibility positively leads to effort expectation and normative influence.
Automatic Identification of MALDI-TOF MS Database Using Classical Bordetella Species Isolates
Authors: Liu Yamin , Cui Junwen , Qie Chunhua , Jiang Bei , Li Ying , Zhao Xiaoyun
Published: June 2022

Objective. To evaluate and expand the automatic identification and clustering of clinical Bordetella species by MALDI-TOF MS. Methods. Twenty-eight field isolated strains, identified by whole-gene sequencing analysis, were analyzed by MALDI-TOF MS, and the spectra obtained were used to replenish the internal database of the manufacturer. To evaluate and expand the robustness of the database, MALDI-TOF MS identified 91 clinical isolates (except those used for implementation). A distance tree based on mass spectrometry data is constructed to confirm similarity and clusters of each clinical Bordetella species by using the MALDI Biotyper 3.1 software. Results. In this research, when we used the implemented Bruker Daltonics database in our laboratory, 91 clinical isolates were identified at the genus level (100%) and 93.4% were identified at the species level (85/91). We performed proteomics analysis and divided these 91 isolates into cluster I (2.2%) and cluster II (97.8%). The largest group is cluster II (

n
=
89

isolates), which has been divided into two subclusters. Trees created by analyzing the protein mass spectra of the three species of the clinical isolates reflected their classification. Conclusion. MALDI-TOF MS may present an attractive alternative to automatically confirm and cluster the fastidious bacteria difficult to culture. Extension of identification of the MALDI-TOF MS database is viably fast, more efficient, and alternative to conventional methods in confirming the classical Bordetella species. This strategy could promote the epidemiological and taxonomic research of this important pathogen.
Effect of Graphene Fillers on the Water Absorption and Mechanical Properties of NaOH‐Treated Kenaf Fiber‐Reinforced Epoxy Composites
Authors: Raj R. Ranga , Sathish S. , Mansadevi T. L. D. , Supriya R. , Sekar S. , Patil Pravin P. , Tonmoy Mahtab Mashuq
Published: June 2022

This paper is focused on developing composites using kenaf fibers, epoxy polymer, and incorporation of graphene fillers. The kenaf fibers are treated with 5% NaOH to remove the hydrophilic nature and reinforced it with the hydrophobic matrix. The composites are fabricated using the compression moulding technique by keeping 60 wt.% epoxy as constant, and the graphene and kenaf fiber weights are changed accordingly. The samples for tensile, flexural, impact, hardness, and water absorption tests are prepared as per the ASTM D3039, D790, D256, D2240, and D572 standards, respectively. The effect of graphene fillers in the 5% NaOH‐treated kenaf fibers reinforced with the epoxy matrix is tested. Among the various samples, sample 4 which has 6% graphene addition in the epoxy matrix reinforced with 5% treated kenaf fiber displayed the highest tensile strength of 63 MPa, flexural strength of 97 MPa, impact strength of 9.56 kJ/m2, hardness value of 97, and lower water absorption of 5.13%. This is due to the proper dispersion of graphene fillers in the matrix which caused better interfacial adhesion between the fiber and matrix. The water absorption test showed the lowest value in sample S5 as the graphene fillers obstruct water penetration in the fibers. SEM analysis is done on the prepared samples to study the surface flaws and structural changes.
Gel Immersion Endoscopic Mucosal Resection (EMR) for Superficial Nonampullary Duodenal Epithelial Tumors May Reduce Procedure Time Compared with Underwater EMR (with Video)
Authors: Yamashina Takeshi , Shimatani Masaaki , Takahashi Yu , Takeo Masahiro , Saito Natsuko , Matsumoto Hironao , Kasai Takeshi , Kano Masataka , Sumimoto Kimi , Mitsuyama Toshiyuki , Marusawa Hiroyuki , Nishio Akiyoshi , Yuba Takafumi , Seki Toshihito , Naganuma Makoto
Published: June 2022

Background/Aims. Underwater endoscopic mucosal resection (UEMR) has recently attracted attention as a safe and effective method for superficial nonampullary duodenal epithelial tumors (SNADETs). However, water is a fluid and it does not remain in all areas, and because it mixes easily with other substances, it sometimes becomes cloudy and reduces visibility. A new endoscopic gel product that does not mix with fluids and tends to remain in the injected area has recently been applied to EMR and is known as gel immersion EMR (GIEMR) or under-gel EMR. Here, we evaluated the endoscopic outcomes of UEMR and GIEMR. Materials and Methods. This was a retrospective cohort study conducted in two municipal hospitals. We identified 24 patients with SNADETs of 3–18 mm in diameter who underwent UEMR or GIEMR. One lesion was excluded from the analysis because it was found to be in the stomach after surgery. The primary outcome was procedure time. Results. GIEMR significantly reduced the procedure time compared with UEMR (5 min vs. 10 min,

P
=
0.016

). There was no significant difference between the UEMR and GIEMR groups for en bloc resection rate (93% vs. 100%,

P
=
1.0

) and R0 resection rate (57% vs. 80%,

P
=
0.39

). No serious complications were observed in either group. Conclusions. GIEMR of SNADET has the potential to reduce procedure time compared with UEMR and may be particularly effective in areas where immersion in water is difficult.
LiDAR: A Light-Weight Deep Learning-Based Malware Classifier for Edge Devices
Authors: Kim Jinsung , Ban Younghoon , Jeon Geochang , Kim Young Geun , Cho Haehyun
Published: June 2022

With the advent of the 5G network, edge devices and mobile and multimedia applications are used a lot; malware appeared to target edge devices. In the fourth quarter of 2020, 43 million pieces of malware targeting mobile devices occurred. Therefore, a lot of researchers studied various methods to quickly protect users from malware. In particular, they studied detecting malware for achieving the high accuracy with deep learning-based classification models on mobile devices. However, such deep learning-based classifiers consume a lot of resources, and mobile devices have limited hardware resources such as RAM and battery. Therefore, such approaches are difficult to be used in the mobile devices in practice. In this work, we study how a deep learning classifier classifies malware and proposed a novel approach to generate a light-weight classifier that can efficiently and effectively detect malware based on the insight that malware exhibits distinctive features as they are programmed to perform malicious actions such as information leaks. Therefore, by analyzing and extracting distinctive features used by a deep learning classifier from malicious dataset, we generate a light-weight rule-based classifier with high accuracy to efficiently detect malware on edge devices called LiDAR. On an edge device, LiDAR detects malware with 94% accuracy (F1-score) and 85.67% and 328.24% lower usages for CPU and RAM, respectively, than a CNN classifier, and showed the classification time 454.37% faster than the classifier.
CFD Model of Turboprop Engine Nacelle Airflow for Ground Idle Condition
Authors: Simeunović Goran , Popelka Lukáš , Fišer Jaromír , Vyhlídal Tomáš , Hatschbach Petr
Published: June 2022

Research in this paper is concerned on bay cooling of the new type of turboprop engine. The considered engine integration substantially increases the temperature inside the front nacelle compartment. In order to achieve the optimal temperature conditions for engine parts inside the nacelle for all operating regimes and engine/nacelle regime after aircraft landing at heat soak-back, a new bay cooling system is proposed. Proposed cooling system consists of standard NACA inlets at the front of the nacelle, two additional groups of gills on rear part of front nacelle compartment, Zone1, and standard nacelle gaps (around exhausts), plus spinner gap. The sizing approach in the given multicriteria task is based on given temperature ratings of the engine accessories. Moreover, for structural reasons, metal skin and stiffeners in the front part of the nacelle should be maintained below the defined temperature limits. Using a 3D CFD model of the front nacelle compartment, this compartment is analysed, utilizing the software ANSYS. The benchmark testing of the considered turboprop engine of ATP type, performed by manufacturer, is used for defining the all necessary boundary conditions in this research.
Assessment of Bird Species Composition, Relative Abundance, and Distributions in East Gojjam Wetland Habitats, Ethiopia
Authors: Gibru Amare , Biru Yihew
Published: June 2022

Many bird species depend on wetlands and the surrounding habitats. However, the status of these wetlands, as well as their biodiversity, is poorly understood and maintained. From January to February 2021, we assessed the compositions, relative abundances, and distributions of bird species throughout five wetland habitats in the East Gojjam zone. In each study site, systematic random sampling techniques were applied at a 4 km interval along the wetland habitats. Bray–Curtis cluster analysis was conducted using PAST software. During the study period, Simpson’s Index and Shannon–Wiener Index were also used to assess the diversity of bird species at various study sites. As a result, a total of 55 bird species from 20 families and 9 orders were identified. During the study period, 49 species were classified as least concern, two were critically endangered species, two were vulnerable species, two were endangered species, and one was an endemic species. During the study, overgrazing and agricultural expansion were identified as threats to biodiversity. To conserve the biological richness of these ecosystems, a wetland conservation strategy and a sustainable usage system are required.
Melting Heat Transition in a Spinning Flow of Silver‐Magnesium Oxide/Engine Oil Hybrid Nanofluid Using Parametric Estimation
Authors: Bilal Muhammad , Gul Taza , Mouldi Abir , Mukhtar Safyan , Alghamdi Wajdi , Bouzgarrou Souhail Mohamed , Feroz Nosheen
Published: June 2022

This study reports the three‐dimensional (3D) flow of Ag‐MgO hybrid nanofluid (HNF) over a spinning disc of flexible thickness in the presence of modified Fourier law. The HNF is contained of silver and magnetic nanoparticulate in the base fluid engine oil. The energy transition has been examined in the involvement of melting heat propagation. The highly nonlinear system of partial differential equations (PDEs) is processed by adopting the proper similarity conversions to attain the coupled ODE system. The obtained system of modeled equations is numerically solved by employing the Parametric Continuation Method (PCM). The nature of various constraints, as opposed to the velocities, energy, and mass transmission, is portrayed and described. In comparison to the simple nanofluid flow, the hybrid nanoliquid flow’s velocity and heat conduction are observed to have a significant influence. As a result, the functionality of the hybrid nanoliquid is significantly superior to that of the conventional nanofluid. The positive variation in power‐law exponent n and Reynold number Re significantly enhances the fluid velocity. The effect of both melting coefficient and thermal relaxation term reduces fluid temperature.
Psychological Factors in Training of Basketball Players to Improve Their Shooting Accuracy
Authors: Lu YunFei , Li WenHao
Published: June 2022

The study reveals that technical movements, training level, and physical quality of current athletes have become more and more similar. The game performance of the same level of athletes is very different. One of the most important reasons is psychological quality. The purpose of studying the psychological factors affecting shooting accuracy in basketball games is to improve the shooting accuracy of the player. By using literature, observation, and interview methods, some psychological factors such as emotion, self-confidence, attention, and the mental quality of the players are discussed. This study aims to provide some references for effectively improving the shooting performance of basketball players in the game. Analyzing these psychological factors shows that the victory or defeat of the game depends on the technical and tactical level of the players and it also depends on whether the player can overcome the influence of various psychological factors. Therefore, by training and motivation, the player will not lose confidence. By using systematic and scientific training methods, focusing on body training and psyche, the psychological barriers of athletes will effectively be overcome. It will also allow them to maintain and improve their shooting hit rate. Finally, using the existing approaches, the proposed study uses data mining and inferences to evaluate the psychological factors that affect the training of basketball players to enable them to improve their shooting accuracy.
Innovative Practice of Music Education in the Universities in the Context of 5G Network
Authors: Huang Shaohan
Published: June 2022

Music education is among the most significant subjects covered in providing high-quality education in Chinese universities and colleges. Music education is critical to providing high-quality education to students. It contributes significantly to the development of students’ creative motivation, inventive capacity, and personality development. Music education provides excellent outcomes in music instruction and fosters students’ original thinking and comprehensive abilities, and therefore supports the overall development of high-quality education. With the development of the educational system, it is becoming more vital to teach students a high level of musical literacy. With the advent of 5G mobile communication, it will become one of the core technologies in Chinese music education, providing an innovative framework for music education. In this study, a novel music education model is proposed for the development of music education using the GTZAN dataset which is comprised of 100 distinct specimens for every genre and ten various kinds of music. The dataset is normalized to prepare it for further processing and the characteristics of the song are retrieved using a technique called spectrum-based feature extraction (SBF). Bi-recurrent neural networks (Bi-RNN). are used to classify objects in space. An improved TCP congestion control algorithm (ITCCA) is proposed for efficient data transmission between the 5G networks. To optimize the performance of the transmission protocol, the honey bee optimization algorithm is employed. The performance of the proposed model is examined and contrasted with that of the currently used approaches. The proposed model shows high performance in terms of throughput, average delay, and packet delivery ratio. The model has the potential to successfully integrate 5G technologies and music education and provide the students with rich and diversified teaching materials and flexible instructional formats.
Decision-Making and Management Method of Public Cultural Service Consumption Preference Based on Multisource Big Data Fusion
Authors: Zhong Baojing , Zhang Chuan , Li Bo
Published: June 2022

Public cultural service is a product of a new era, and it is also an important product of the urbanization process. Public cultural services can not only improve the cohesion between the government and residents, but also it can improve the living standards and happiness index of residents. The public cultural service product is not only a symbol of the city, and it is more important to meet the needs and satisfaction of the residents. It needs to truly understand the preferences and needs of residents and then build public cultural service products according to their preferences. The social public cultural service model under the traditional model is dominated by the willingness of the government, which makes it difficult to truly understand the preferences and needs of residents. This paper uses data fusion and neural network methods to study the influencing factors in public cultural services. The results show that the data fusion method can capture the relationship between the factors of the public cultural service and the residents' preference, and the error of the four factors is less than 2.83% for the classification of the public cultural service. The largest prediction error is only 3.11%, and this part of the error comes from residents' satisfaction with the public cultural industry, which is an acceptable error.
Evaluation of Japanese Teaching Quality Based on Deep Neural Network
Authors: Liu Hailing
Published: June 2022

The 21st century is an era of rapid development of information and frequent international exchanges, and Japanese language teaching has received increasing attention. Because of this, colleges and universities are now focused on improving the quality of Japanese education, both now and in the future. We need to boost the whole management of teaching quality, notably the assessment of instructors’ teaching quality, in order to improve teaching quality. However, because a number of factors influence the quality of instruction, and each factor’s weight varies, the evaluation results are difficult to express in a mathematical analytical formula, resulting in a complex nonlinear classification problem that traditional classification methods cannot solve well. As a new technology, as a result of the artificial neural networks (ANNs) fundamental qualities, it has been extensively applied in different evaluation issues for pattern recognition, nonlinear classification, and other research. This subject introduces the optimized deep neural network theory into Japanese teaching quality evaluation and completes the following work: (1) the algorithm of discrete Hopfield neural network is introduced in detail, and the neural network theory is introduced into teaching evaluation. (2) Then, based on the evaluation data of teachers’ teaching quality in a school, a large number of simulation experiments and training were carried out, and a neural network model for evaluation of teachers’ teaching effect was constructed and designed. Experiments reveal that the neural network model proposed in this paper is a nonlinear mapping method, which increases the evaluation’s dependability and makes the outcomes more effective and objective.
Simultaneous Gemcitabine and Percutaneous CT-Guided Irreversible Electroporation for Locally Advanced Pancreatic Cancer
Authors: Ma Yangyang , Xing Yanli , Li Hongmei , Liang Bing , Li Rongrong , Li Jianyu , Li Zhonghai , Lin Mao , Niu Lizhi
Published: June 2022

Background. Irreversible electroporation (IRE) is a new local ablation technique for pancreatic cancer. The aim of this study is to analyse the safety and effectiveness of simultaneous gemcitabine and percutaneous CT-guided IRE for locally advanced pancreatic cancer (LAPC). Materials and Methods. From October 2016 to January 2018, 61 patients with LAPC who received simultaneous gemcitabine and IRE therapy (GEM-IRE group, n = 31) or IRE alone therapy (IRE group, n = 30). Routine intravenous gemcitabine chemotherapy was performed 2 weeks after IRE in both groups. Results. Technical success rates were 90.0% (27/30) and 93.3% (28/30) in the GEM-IRE and IRE groups. Compared with the IRE group, the GEM-IRE group exhibited longer overall survival (OS), local tumor progression free survival (LTPFS), and distant disease free survival (DDFS) from IRE (OS, 17.1 vs. 14.2 months,

p
=
0.031

; LTPFS, 14.6 vs. 10.2 months,

p
=
0.045

; DDFS, 15.4 vs. 11.7 months,

p
=
0.071

). Multivariate Cox regression analysis results suggested that tumor volume ≤37 cm3 and simultaneous gemcitabine with IRE were significant independent prognostic factors of OS, LTPFS, and DDFS. Four major adverse reactions occurred; all of them were resolved after symptomatic treatment. Conclusions. Simultaneous gemcitabine and percutaneous CT-guided IRE therapy model was effective and well-tolerated therapeutic strategy in LAPC patients.
Design of Counselor Job Matching and Ideological and Political Education Methods Integrating Deep Learning Model
Authors: Wu Rong , Xu Jian
Published: June 2022

In the process of civic and political education, counselors should not only provide good service guidance for students’ learning but also deeply understand students’ ideological dynamics and psychological conditions. This would help to guide them in establishing healthy ideological concepts and moral qualities. The continuous development of ideological education cannot be achieved without the assistance of an experienced counselor team. There is a nationwide requirement to strengthen the professionalism of college counselors which is presently lagging. There exists a gap between the demand of the job seekers and the relevant products who fail to meet the need of the college administrators, working counselors, and other groups. The present paper focuses on providing solutions to the current problems pertinent to inaccurate matching of counselor positions in ideological and political education, the lagging information feedback, and the existence of imperfect early warning intervention mechanism. The paper proposes an integrated deep learning model which automates the learning of a large number of college students’ user behaviors using deep learning algorithms thereby incorporating early warning classifiers. This helps to establish a model enabling accurate counselor job matching and ideological and political education methods. Simulation is used to verify the effectiveness of the model using relevant databases which establishes the superiority of the proposed method in resolving mismatch issues in human resources, handles imbalances in actual effectiveness, and also ensures lagging information feedback in the process of providing dynamic early warning in case of college and university level ideological and political education.
Metal Jewelry Craft Design Based on Computer Vision
Authors: Li Nan
Published: June 2022

Combining computer vision technology with process design, a new design and production method is obtained, which breaks through the limitations of traditional jewelry creation and provides new possibilities for the realization of complex jewelry structures. When technology no longer becomes the bottleneck of artistic expression, the space of art will be greatly expanded. Science and technology leading design method has become a new way to assist jewelry artists in subjective creation. According to various thoughts and ideas in design, establishing the corresponding algorithm rules and parameters can generate the scheme through calculation. The design result obtained in this way not only has a scientifically logical basis but also obtains the result beyond the normal imagination space due to the intelligent design process. This paper tries to apply computer vision technology to modern jewelry design, analyzes several aspects of computer vision application in process design, and combines the latest technical means to put forward algorithms for verification. The results prove that computer vision can improve the efficiency of crafts design significantly.
Tumor Detection on Microarray Data Using Grey Wolf Optimization with Gain Information
Authors: Sree Devi K. Dhana , Karthikeyan P. , Moorthy Usha , Deeba K. , Maheshwari V. , Allayear Shaikh Muhammad
Published: June 2022

Microarray data are becoming a more essential source of gene expression data for interpretation and analysis. To improve the detection accuracy of tumors, the researchers try to use the lowest feasible collection of the most gene expression studies, and relevant gene expression patterns are found. The purpose of this article is to use a data mining strategy and an optimized feature selection method focused on a limited dense tree forest classifier to evaluate and forecast colon cancer data. More specifically, merging the “gain information” and “Grey wolf optimization” was incorporated as a feature selection approach into the random forest classifier, to improve the prediction model’s accuracy. Our suggested technique can decrease the load of high-dimensional data, and it allows quicker computations. In this research, we provided a comparison of the analysis model with feature selection accuracy over model analysis without feature selection accuracy. The extensive experimental findings have shown that the suggested method with selecting features is beneficial, outperforming the good classification performances.
[Retracted] Automatic Segmentation of MRI of Brain Tumor Using Deep Convolutional Network
Authors: Zhou Runwei , Hu Shijun , Ma Baoxiang , Ma Bangcheng
Published: June 2022

Computer‐aided diagnosis and treatment of multimodal magnetic resonance imaging (MRI) brain tumor image segmentation has always been a hot and significant topic in the field of medical image processing. Multimodal MRI brain tumor image segmentation utilizes the characteristics of each modal in the MRI image to segment the entire tumor and tumor core area and enhanced them from normal brain tissues. However, the grayscale similarity between brain tissues in various MRI images is very immense making it difficult to deal with the segmentation of multimodal MRI brain tumor images through traditional algorithms. Therefore, we employ the deep learning method as a tool to make full use of the complementary feature information between the multimodalities and instigate the following research: (i) build a network model suitable for brain tumor segmentation tasks based on the fully convolutional neural network framework and (ii) adopting an end‐to‐end training method, using two‐dimensional slices of MRI images as network input data. The problem of unbalanced categories in various brain tumor image data is overcome by introducing the Dice loss function into the network to calculate the network training loss; at the same time, parallel Dice loss is proposed to further improve the substructure segmentation effect. We proposed a cascaded network model based on a fully convolutional neural network to improve the tumor core area and enhance the segmentation accuracy of the tumor area and achieve good prediction results for the substructure segmentation on the BraTS 2017 data set.
Research on Soft Sensing Method of Straw Ethanol Fermentation Process Based on BSVR
Authors: Xu Feng , Tang Hong-yu , Wang Bo , Zhu Xiang-Lin
Published: June 2022

Straw fermented fuel ethanol is a complex process with multivariable, large lag, and strong nonlinearity. It is difficult to directly measure the key parameters such as ethanol concentration and cell concentration online. Aiming at the problem, a soft sensing model of straw fermented ethanol based on improved support vector regression (SVR) is proposed. Based on the analysis of the process of ethanol production from straw fermentation, the Bayesian method is used to optimize the support vector regression (BSVR). And the concepts of generation a priori and generation likelihood are introduced to optimize the data prediction model. The comparative experiment of model training and testing is carried out. The simulation results show that the proposed BSVR method is better than SVR. It can improve the generalization ability of data and the anti-interference of the model, and its prediction accuracy and stability are higher.
A Modern Approach towards an Industry 4.0 Model: From Driving Technologies to Management
Authors: Tsaramirsis Georgios , Kantaros Antreas , Al-Darraji Izzat , Piromalis Dimitrios , Apostolopoulos Charalampos , Pavlopoulou Athanasia , Alrammal Muath , Ismail Zamhar , Buhari Seyed M. , Stojmenovic Milos , Tamimi Hatem , Randhawa Princy , Patel Akshet , Khan Fazal Qudus
Published: June 2022

Every so often, a confluence of novel technologies emerges that radically transforms every aspect of the industry, the global economy, and finally, the way we live. These sharp leaps of human ingenuity are known as industrial revolutions, and we are currently in the midst of the fourth such revolution, coined Industry 4.0 by the World Economic Forum. Building on their guideline set of technologies that encompass Industry 4.0, we present a full set of pillar technologies on which Industry 4.0 project portfolio management rests as well as the foundation technologies that support these pillars. A complete model of an Industry 4.0 factory which relies on these pillar technologies is presented. The full set of pillars encompasses cyberphysical systems and Internet of Things (IoT), artificial intelligence (AI), machine learning (ML) and big data, robots and drones, cloud computing, 5G and 6G networks, 3D printing, virtual and augmented reality, and blockchain technology. These technologies are based on a set of foundation technologies which include advances in computing, nanotechnology, biotechnology, materials, energy, and finally cube satellites. We illustrate the confluence of all these technologies in a single model factory. This new factory model succinctly demonstrates the advancements in manufacturing introduced by these modern technologies, which qualifies this as a seminal industrial revolutionary event in human history.
Acupuncture Combined with Traditional Chinese Medicine and Drug Therapy for the Treatment of Cerebral Infarction (Phlegm-Blood Stasis Syndrome) and Carotid Atherosclerotic Plaque: A Preliminary Randomized Controlled Study
Authors: Wang Wentong , Liu Zhuli , Wu Yongxiong
Published: June 2022

Carotid atherosclerotic plaque (CAP) is one of the leading causes of cerebral infarction. Western medicine usually uses lipid-lowering drugs to stabilize plaques. Currently, studies reporting on drugs that can reduce plaques are lacking. Here, we performed a randomized controlled study to investigate the effectiveness of acupuncture combined with drug therapy (TCM and Western) to treat cerebral infarction (phlegm-blood stasis syndrome) and CAP. The control group was treated with atorvastatin calcium tablets (20 mg/d, po for 15 days). The treatment group received atorvastatin calcium tablets 20 mg, traditional Chinese medicine (TCM) decoctions (two matured substance decoction plus peach kernel and Carthamus four substance decoction plus Chinese hawthorn fruit 20 g, gold theragran 20 g, and red yeast rice 3 g), and acupuncture therapy, once daily for 15 days as one treatment course. The patients’ neurological deficit score, ultrasonic testing of the carotid artery, and lipoprotein-associated phospholipase A2 (Lp-PLA2) were evaluated. Our findings showed no significant difference in the evaluated indices between the two groups before treatment (

P
>
0.05

). However, compared with the control group after 15 days of treatment and within each group before and after treatment, the differences were significant (

P
<
0.05

). In conclusion, acupuncture combined with drug therapy demonstrated promising effectiveness in treating cerebral infarction (phlegm-blood stasis syndrome) and CAP.
Management of External Inflammatory Root Resorption following Tooth Autotransplantation Using a Modified Combination of Triple Antibiotics
Authors: Asgary Saeed , Parhizkar Ardavan
Published: June 2022

External inflammatory root resorption (EIRR) is one of the most undesirable potential repercussions of various types of trauma and traumatic injuries to the tooth and its structure. This detrimental phenomenon may lead to severe complications, the consequent destruction of dental tissues, and eventual tooth loss. In the presented case, following the autogenous transplantation of tooth #17 as the host-tissue replacement for tooth #18, signs of EIRR were radiographically detected after 6 months. However, the thorough preparation of root canals, which involves complete cleaning and shaping, in addition to the application of a modified combination of triple antibiotics, consisting of penicillin G, metronidazole, and ciprofloxacin (PMC), managed to arrest EIRR. Moreover, the obturation of root canals using calcium-enriched mixture (CEM) cement as the endodontic biomaterial as well as the proper management of EIRR resulted in the healing of periradicular pathosis, abatement of clinical symptoms, and finally refunctionalisation of the tooth during a follow-up period of 2 years. It seems that the appropriate disinfection of the root canal system using PMC next to the prevention of microbial recontamination using CEM cement can perfectly manage EIRR.
Region Dual Attention-Based Video Emotion Recognition
Authors: Liu Xiaodong , Xu Huating , wang Miao
Published: June 2022

To solve the emotional differences between different regions of the video frame and make use of the interrelationship between different regions, a region dual attention-based video emotion recognition method (RDAM) is proposed. RDAM takes as input video frame sequences and learns a discriminatory video emotion representation that can make full use of the emotional differences of different regions and the interrelationship between regions. Specifically, we construct two parallel attention modules: one is the regional location attention module, which generates a weight value for each feature region to identify the relative importance of different regions. Based on the weight, the emotion feature that can perceive the emotional sensitive region is generated. The other is the regional relationship attention module, which generates a region relation matrix that represents the interrelationship of different regions of a video frame. Based on the region relation matrix, the emotion feature that can perceive interrelationship between different regions is generated. The outputs of these two attention modules are fused to produce the emotional features of video frames. Then, the features of video frame sequences are fused by attention-based fusion network, and the final emotion feature of the video is produced. The experimental results on the video emotion recognition data sets show that the proposed method outperforms the other related works.
Key Technology of the Medical Image Wise Mining Method Based on the Meanshift Algorithm
Authors: Cui Jinli , Wang Yadong , Wang Ke
Published: June 2022

Mean-shift originally refers to the mean vector of the offset. The algorithm idea is to assume that the data sets of different clusters conform to different probability density distributions, and the area with high sample density corresponds to the center of the cluster. With the wide application of hospital information system, especially the popularity of the meanshift algorithm in the outpatient system, it has greatly improved the efficiency of medical staff. Medical imaging refers to the technology and process of obtaining internal tissue images of the human body or a certain part of the human body in a noninvasive manner for medical treatment or medical research. It contains the following two relatively independent research directions: medical imaging system and medical image processing. In this paper, we expect to improve the mining ability of medical image information with the help of the meanshift algorithm based on the key technology of the medical image intelligent mining algorithm. This paper proposes a method to enhance image feature extraction and data mining and how to apply relevant analysis rules for mining. Applying this integrated algorithm to extract simplified rules is more beneficial to people’s understanding than the raw data and helps doctors quickly understand the patient’s condition.
Numerical Simulation and Application of Zero‐Thickness Contact Surface Element with Variable Shear Stiffness on Pile Foundation
Authors: Wang You , Xiong Fan , Ren Jialin
Published: June 2022

In this study, a contact surface constitutive model with zero‐thickness unit and variable shear stiffness was improved based on the statistical damage constitutive model. The model parameters were derived by means of the shear stress‐shear displacement curve of the pile‐soil contact surface, and the model parameters under different normal stresses were obtained by the linear interpolation method. At the same time, the influence of the interpolation interval range on the model parameters was explored. The shear stiffness adjustment factor was introduced, and the improved pile‐soil contact surface constitutive model was applied to the numerical simulation of pile‐soil contact surface shear calculations using the fish language embedded in FLAC 3D, and the variation of monopile bearing characteristics and ultimate bearing capacity were investigated and analyzed. The results show that the improved contact surface constitutive model is able to reflect the nonlinear variation of shear stiffness, and that different normal stresses correspond to different fitting parameters, demonstrating the depth effect of the model. The accuracy of the model parameters reduces as the interpolation interval increases, and the interpolation results are more accurate when the interval range is smaller. The numerical model accurately simulates the pile‐soil contact surface shear calculation and the monopile bearing calculation, and the simulation results of the ultimate pile bearing capacity are closer to the results computed by the equations in the Chinese code. At the same time, the variation law of pile axial force and pile lateral frictional resistance along the depth direction and the variation of pile ultimate bearing capacity under different working conditions are reasonable, which shows the validity of the contact surface principal structure model and the reasonableness of numerical calculation in this study.
Implementation of Missing Data Imputation Schemes in Face Recognition Algorithm under Partial Occlusion
Authors: Appati Justice Kwame , Adu-Manu Kofi Sarpong , Owusu Ebenezer
Published: June 2022

Face detection and recognition algorithms usually assume an image captured from a controlled environment. However, this is not always the case, especially in crowd control under surveillance or footage from a crime scene, where partial occlusions are unavoidable. Unfortunately, these occlusions have an adverse effect on the performance of these classical recognition algorithms. In this study, the performance of some selected data imputation schemes is evaluated on SVD/PCA frontal face recognition algorithm. The experiment was done on two datasets: Jaffe and MIT-CBCL, with immediate confirmation of the adverse effect of occlusion on the facial algorithm without implementing the imputation scheme. Further experimentation shows that IA is an ideal missing data imputation scheme that works best with the SVD/PCA facial recognition algorithm.