bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Compositional Effect on Dielectric and Variable Range Hopping Mechanism of Dysprosium Doped BNT-BT Ceramics
Authors: Sahoo S. , Badapanda T. , Sarangi S. , Tripathy Satya N.
Published: May 2022

The manuscript describes the dielectric properties and conduction mechanism of Dysprosium doped 0.92Na0.5Bi0.5TiO3–0.08BaTiO3 (BNT-BT8) composition prepared by solid-state reaction method. Rietveld refinement study of X-ray diffraction data shows that all the samples exhibit a combination of rhombohedral (R3c) and tetragonal (P4mm) crystallographic phases. It is found that with an increase in Dy content, the pseudo-cubic lattice parameter of R3c phase gradually decreases. The modification in the microstructure of the doped samples was investigated by scanning electron microscope. The temperature and frequency-dependent dielectric data show enhancement of the dielectric dispersion in the region of the dielectric peak with doping. To achieve more insight into the evolution of disorder in the system, the Landau-Devonshire (LD) cluster theory was employed to evaluate the local order parameter. The change of ac-conductivity with the temperature of the studied samples was analyzed using Arrhenius’s law and Mott’s variable range hopping mechanism. The fitting of the model parameters such as density of states (N(EF)), hopping length (RH), and hopping energies (WH) were calculated for all the compositions. The activation energy associated with the samples was calculated from the Arrhenius diagram, which reflects the conduction process is different at different temperature ranges.
Application of Carbon Nanocomposite Sensing and Functional Magnetic Nanomaterial in the Sports Industry and Its Biosecurity Research
Authors: Li Zhenpeng , Li Zhigan , Zhang Zecheng
Published: May 2022

Nanomaterial science is a particularly important research field in modern science and technology, which has attracted wide attention and research from all walks of life since its inception and has formed a relatively perfect category of science and technology. The purpose of this paper is to explore the application of carbon nanocomposite sensing and functional magnetic nanomaterial in the sports industry and their biosecurity research. This paper first discusses the research progress of nanocomposite in biosecurity and concludes that chemical modification can reduce the negative biological effects of nanomaterial, then puts forward the research methods on nanocomposite in the sports industry, and then selects nanosilver composites to explore and study the effects of different particle sizes and concentrations on the antibacterial properties of Aspergillus and test and characterize the properties of nanocomposite. The stability of nanocomposite modification electrodes is also explored. The results show that nanosilver has excellent bactericidal effects on both AF293 and Gra04. In particular, 8.08 × 10−5 mol/L and 4.03 × 10−5 mol/L nanosilver has a bacteriological rate of 100%, indicating that the growth of all fungal spores can be completely inhibited, and when the content of 10 wt% is found, the thermal conductivity of PHBE‐MWNT pressure‐sensitive composite materials is achieved 0.6975 W/(m‐K), 1.8 times the MWNT pressure‐sensitive glue, higher than the pure pressure‐sensitive glue about 255%.
IL-37 Expression in Patients with Abdominal Aortic Aneurysm and Its Role in the Necroptosis of Vascular Smooth Muscle Cells
Authors: Ding Yan , Wang Yue , Cai Yifan , Pan Chengliang , Yang Chao , Wang Miao , Qi Xiaoyu , Ye Jing , Ji Qingwei , Yu Jian , Xu Wenbin , Yu Kunwu , Zeng Qiutang
Published: May 2022

Background. Our previous studies have shown that interleukin- (IL-) 37 plays a protective role in patients and animal models with coronary artery disease. However, the role of IL-37 in patients with abdominal aortic aneurysm (AAA), another artery disease, is yet to be elucidated. Methods and Results. AAA tissues and plasma samples were obtained from patients with or without surgical intervention. Normal renal aortic tissues were collected from kidney transplant donors. Our findings established that in AAA, IL-37 was distributed in endothelial cells, macrophages, and vascular smooth muscle cells (VSMCs) and that it was chiefly concentrated in VSMCs. Furthermore, the expression was found to be downregulated compared with that in normal artery tissues. Immunofluorescence showed that, unlike normal arteries, IL-37 was translocated to the nucleus of VSMCs in AAA. Moreover, in patients with AAA, the expressions of IL-37, IL-6, and tumor necrosis factor- (TNF-) α were increased in the plasma in comparison with the healthy controls. Correlation analysis revealed that IL-37 was positively correlated with IL-6, TNF-α, age, aneurysm diameter, and blood pressure. Furthermore, human aortic vascular smooth muscle cells (HASMCs) were stimulated with angiotensin II (AngII) in vitro to simulate smooth muscle cell (SMC) damage in AAA. A decrease in IL-37 expression and an increase in receptor-interacting serine/threonine-protein kinase 3 (RIPK3) expression were observed in HASMCs stimulated with AngII. On this basis, inhibition of RIPK3 with GSK’872 significantly attenuated necroptosis. Moreover, the necroptosis rates were significantly lowered in HASMCs treated with recombinant IL-37, whereas the rates were enhanced when the cells were depleted of the interleukin. Immunoblotting results showed that both exogenous and endogenous IL-37 could affect the expressions of RIPK3, NLRP3, and IL-1β. Also, the phosphorylation of RIPK3 and p65 was affected. Meanwhile, IL-37 promoted the transition of SMC from proliferative type to contractile type. Conclusions. The expression of IL-37 in VSMCs decreases in patients with AAA, whereas IL-37 supplementation suppresses RIPK3-mediated necroptosis and promotes the transition of VSMCs from proliferative to contractile type.
Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images
Authors: Latha S. , Muthu P. , Dhanalakshmi Samiappan , Kumar R. , Lai Khin Wee , Wu Xiang
Published: May 2022

Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.
Research on Interface Slip of Composite Reinforced Steel Truss-Concrete Test Beam and Its Calculation and Analysis Method
Authors: Zhou Zhixiang , Zeng Xingqi , Zou Yang , Li Chengjun , Den Guojun , Zhang Liwen , Wang Rui
Published: May 2022

In this study, a model of test beam in negative moment area is designed, and the slip characteristics of test beam under overlimit static load and variable amplitude cyclic load are studied, respectively. By constructing the relationship between shear stiffness and slip parameters of the test beam, the deformation calculation method of the test beam under different fatigue cycles is derived, and the accuracy of the calculation model is verified by experiments. The results show that the maximum slip value (0.038 mm) of static load after 10,000 times of limited fatigue load is increased by 58.3% compared with that before overlimit fatigue load is applied (0.024 mm). When the fatigue cycle is 0-2 million times, the total slip is between 0.019 and 0.026 mm and the residual slip percentage is between 4.17 and 8.33%. The maximum residual values are 0.022 mm, 0.027 mm, and 0.028 mm after 1.5 times, 2 times, and 3 times of overload and variable amplitude fatigue loads, respectively, and the slip values have no obvious fluctuation, all of which show good working performance. The average value of the ratio between the deformation value and the measured value of composite beams is 0.89, and the standard deviation is 4.86%. When the fatigue loading times are more than 2.8 million, the ratio between the calculated value and the measured value is less than 0.85, so the adaptability of the calculation model has certain limitations. On the whole, the calculation model proposed in this study fully considers the factors such as the stiffness and fatigue loading times of composite beams, and the error between the calculated results and the measured results is within 5%, with high accuracy, which can be used as a reference for the actual design.
Performance Enhancement of Double-Layer Microchannel Heat Sink by Employing Dimples and Protrusions on Channel Sidewalls
Authors: Debbarma Dipak , Pandey Krishna Murari , Paul Abhishek
Published: May 2022

High-temperature gradient causes thermal stress and is indirectly responsible for other demerits in nonstacked microchannels. High-temperature gradient is overcome by employing double layers in the heat sink. Utilization of dimple and protrusion in such double-layered sink to enhance overall performance is done in the present numerical study. Before placement of dimples and protrusions on sidewalls of the sink, optimum width and depth of channel have been assessed. Microsinks with the protruded-dimpled bottom layer and microsinks with protruded-dimpled layers are investigated. The parameters such as maximum bottom wall temperature difference (ΔTb), Nusselt number ratio (Nu/Nuo), and thermal performance factor (η) have been evaluated. The impact of aligned and staggered arrangements of dimple and protrusion is also compared. Deionized water as a coolant for the range of Reynolds number of 89–924 is examined. It has been realized that aligned models offer higher heat transfer coefficient, maximum Nu, and minimum ΔTb, but in terms of overall performance, staggered sinks are superior. The heat sink with both layers protruded and dimpled, showing Nu/Nuo and η of 1.36 and 1.32, respectively, is observed as one of the optimum sinks which offers an excellent lowest ΔTb of 1.48 K.
Automatic Recognition Method of Machine English Translation Errors Based on Multisignal Feature Fusion
Authors: Zhang Ruisi , Huang Haibo
Published: May 2022

The current automatic recognition method of machine English translation errors has poor semantic analysis ability, resulting in low accuracy of recognition results. Therefore, this paper designs an automatic recognition method for machine English translation errors based on multifeature fusion. Manually classify and summarize the real error sentence pairs, falsify a large amount of data by means of data enhancement, enhance the effect and robustness of the machine translation error detection model, and add the source text to translation length ratio information and the translation language model PPL into the model input. The score feature information can further improve the classification accuracy of the error detection model. Based on this error detection scheme, the detection results can be used for subsequent error correction and can also be used for error prompts to provide translation user experience; it can also be used for evaluation indicators of machine translation effects. The experimental results show that the word posterior probability features calculated by different methods have a significant impact on the classification error rate, and adding source word features based on the combination of word posterior probability and linguistic features can significantly reduce the classification error rate, to improve the translation error detection ability.
Research on Building Space Model Method Based on Big Data Map Visual Design
Authors: Xu Qiang , He Long
Published: May 2022

In order to explore the architectural space model of design, a visualization method based on big data map is proposed. Referring to the tile pyramid model, a multidimensional aggregation pyramid model (MAP) is proposed, which extends the 2D spatial hierarchical aggregation of tile pyramid to the multidimensional of time/space/attribute and supports the multidimensional hierarchical aggregation of time, space, and attribute. Then, taking spark cluster as the parallel preprocessing tool and HBase distributed database as the persistent storage of map model data, an open-source distributed visualization framework (MAP-Vis) is realized. Then, the BIM model is reconstructed, and a component instance hierarchical splitting strategy based on IFC structure tree is proposed to separate the digital and analog of the original IFC file. The reconstructed IFC model file is transformed into glTF format file, and the dual relationship mapping of geometric space and semantic attributes is completed in the transformation process. Finally, the visibility detection algorithm of BS-AB scene components based on the hierarchical bounding volume (BVH) structure is proposed to eliminate the visibility of building components. The experimental results show that BIMviews is slow to load the IFC file of the experimental object and obtain the model data, with an average of about 40 s, and the Caton is obvious. However, it only takes about 7 s to load glTF file into big data map visualization design by Three.js. It is verified again that glTF format is more suitable for BIM model data than IFC format. The visualization design, display, and interaction based on big data map are based on glTF format. It proves the effectiveness of big data map visualization.
Music Recommendation System and Recommendation Model Based on Convolutional Neural Network
Authors: Zhang Yezi
Published: May 2022

In today’s era of big data with excess information, music is common and everyday, which shows the huge amount of music data. How to obtain one’s favorite music from the massive music database has become a problem, and the emergence of music recommendation systems is also inevitable. In this paper, we take digital piano music as the research object, form comprehensive features using spectrum and notes, design classification methods using convolutional neural networks, and further process the classification results and design recommendation algorithms. The basic method of music recommendation of this algorithm is to determine the structure of the network model, determine the corresponding training model, and improve the parameters on the basis of the typical source network model used in the system experiment. Historical behavior chooses to collect information. Then, it reads the audio data on the system and retrieves it from Mel, which reveals the identity of the music. The classification proposal achieves its goal by denying the similarity between customer preferences and the potential of two musical characteristics. Two recommended methods based on convolutional neural networks are tested in this article. On the whole, the accuracy of the user’s comprehensive feature, recommendation method is higher than the recommendation accuracy rate of the multicategory user. In the comparison experiment of the single-category and multicategory recommendation methods, the average accuracy rate of single-category user feature recommendation is 50.35%; and the recommendation accuracy rate of multicategory user features is higher than the recommendation accuracy rate of single-category user features. The experimental results show that the two recommendation methods can achieve better recommendation results.
Series Type Solution of Fuzzy Fractional Order Swift–Hohenberg Equation by Fuzzy Hybrid Sumudu Transform
Authors: Ullah Abd , Ullah Aman , Ahmad Shabir , Haq Mahmood Ul , Shah Kamal , Mlaiki Nabil
Published: May 2022

There are some confusion and complexity in our everyday lives, as we live in an uncertain environment. In such type of environment, an accurate calculation of the data and finding a solution to a problem is not an easy job. So, fuzzy differential equations are the better tools to model problems in the fuzzy domain. Modeling the real-world phenomenon more accurately requires such operators. Therefore, we investigate the fractional-order Swift–Hohenberg equation in the fuzzy concept. We study this equation under the fuzzy Caputo fractional derivative. We use the fuzzy Sumudu transform to find out the semianalytical solution of the considered equation. To deal with the nonlinear term of the problem, we also use the Adomian decomposition method. To confirm the accuracy of the proposed procedure, we give two test problems. Lastly, we plot the numerical results for various fractional orders, and uncertainty belongs to [0, 1].
Nonlinear Diffusion-Based Multifield Coupling Model for Thermally Enhanced CBM Recovery
Authors: Yang Rui , Liu Weiqun , Lv Jiakun
Published: May 2022

Thermal stimulation is a supplementary technique for enhancing gas recovery from coalbed methane (CBM) reservoirs that has received considerable attention worldwide. Investigating gas and heat transfer in coal seams during thermally enhanced CBM recovery is of great significance for predicting gas production and optimizing the extraction method. Gas diffusion in the coal matrix and coupled multiphysics are two of the most important aspects when analyzing gas migration and heat transport. However, previous studies either neglected the nonlinear diffusion process of gas or only assumed that the diffusion coefficient varies with production time. However, considerable experimental results indicate that the gas diffusion coefficient is not only determined by time but also by temperature, which strongly impacts multifield interaction during gas recovery. Thus, prior diffusion models and coupled models must be modified. In this paper, a time-and-temperature-dependent gas diffusion model is established based on fractal theory and experimental data. The proposed diffusion model is embedded into the coupled thermal-hydro-mechanical model to comprehensively describe the behavior of coal deformation, gas migration, and heat transport during CBM recovery. Additionally, both new diffusion model and coupled model were validated with experimental results or field test data, showing that these developed models are applicable for modeling long-term gas diffusion and gas production. Finally, the coupled model was implemented into COMSOL Multiphysics software, and a series of numerical simulations were conducted. The calculation results showed that heat injection could promote gas desorption and diffusion in the matrix while inhabiting gas flow in fractures near the injection well. Gas dynamic diffusion could inhabit gas migration in both matrix system and fracture system at a later production stage. This also means that ignoring the gas nonlinear diffusion process leads to a severe overestimation of coal permeability and gas production.
Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers
Authors: Parthiban S. , Harshavardhan A. , Neelakandan S. , Prashanthi Vempaty , Alhassan Alolo Abdul-Rasheed Akeji , Velmurugan S.
Published: May 2022

The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics.
Fibroblasts in Scar Formation: Biology and Clinical Translation
Authors: Qian Huan , Shan Yihan , Gong Ruicheng , Lin Danfeng , Zhang Mengwen , Wang Chen , Wang Lu
Published: May 2022

Scarring, which develops due to fibroblast activation and excessive extracellular matrix deposition, can cause physical, psychological, and cosmetic problems. Fibroblasts are the main type of connective tissue cells and play important roles in wound healing. However, the underlying mechanisms of fibroblast in reaching scarless wound healing require more exploration. Herein, we systematically reviewed how fibroblasts behave in response to skin injuries, as well as their functions in regeneration and scar formation. Several biocompatible materials, including hydrogels and nanoparticles, were also suggested. Moreover, factors that concern transformation from fibroblasts into cancer-associated fibroblasts are mentioned due to a tight association between scar formation and primary skin cancers. These findings will help us better understand skin fibrotic pathogenesis, as well as provide potential targets for scarless wound healing therapies.
Emerging Roles of Green-Synthesized Chalcogen and Chalcogenide Nanoparticles in Cancer Theranostics
Authors: Olawale Femi , Oladimeji Olakunle , Ariatti Mario , Singh Moganavelli
Published: May 2022

The last few decades have seen an overwhelming increase in the amount of research carried out on the use of inorganic nanoparticles. More fascinating is the tremendous progress made in the use of chalcogen and chalcogenide nanoparticles in cancer theranostics. These nanomaterials, which were initially synthesized through chemical methods, have now been efficiently produced using different plant materials. The paradigm shift towards the biogenic route of nanoparticle synthesis stems from its superior advantages of biosafety, eco-friendliness, and simplicity, among others. Despite a large number of reviews available on inorganic nanoparticle synthesis through green chemistry, there is currently a dearth of information on the green synthesis of chalcogens and chalcogenides for cancer research. Nanoformulations involving chalcogens such as sulfur, selenium, and tellurium and their respective chalcogenides have recently emerged as promising tools in cancer therapeutics and diagnosis. Similar to other inorganic nanoparticles, chalcogens and chalcogenides have been synthesized using plant extracts and their purified biomolecules. In this review, we provide an up-to-date discussion of the recent progress that has been made in the plant-mediated synthesis of chalcogens and chalcogenides with a special focus on their application in cancer theranostics.
Gambogic Acid and Piperine Synergistically Induce Apoptosis in Human Cholangiocarcinoma Cell via Caspase and Mitochondria-Mediated Pathway
Authors: Yapasert Rittibet , Banjerdpongchai Ratana
Published: May 2022

Most cholangiocarcinoma (CCA) patients undergo chemotherapy as a therapeutic approach due to the disease’s frequently late diagnosis. However, because CCA is resistant to currently available treatments, the prognosis for this cancer is still quite poor. Combination therapy has emerged as a novel and promising strategy in cancer treatment, as monotherapy frequently results in tumor recurrence and drug resistance. Gambogic acid has been shown to have a synergism with other compounds in combating certain cancer cells. Moreover, piperine has been shown to improve the efficacy of numerous chemotherapy drugs and other anticancer natural substances. However, no research has been done on the combination of these two compounds in the treatment of bile duct cancer. In this study, the cytotoxic activity was determined by using the MTT assay, and then, the combined effect was assessed by using the combination index (CI). We found that the combination of gambogic acid and piperine inhibited cell viability more effectively than either treatment alone, and it also demonstrated a synergistically cytotoxic effect against CCA cells. Interestingly, the findings allowed the use of lower concentrations of gambogic acid in cancer treatment when combined with piperine, which could reduce its adverse effect on normal cholangiocytes. Furthermore, the combination of the two compounds increased CCA cell death by inducing apoptosis via both the extrinsic and intrinsic or mitochondria-mediated pathways, as determined by caspase-3, -8, and -9 activity and the reduction of mitochondrial transmembrane potential (ΔΨm). It is possible that the use of these two natural compounds together could be a promising strategy for the treatment of bile duct cancer.
Design of Education Information Platform on Education Big Data Visualization
Authors: Wang Ping , Zhao Pengfei , Li Yingji
Published: May 2022

Big data visualization in education plays an essential role in understanding and uncovering complex educational laws and is increasingly becoming an essential topic in the current research fields of educational information science and educational management. In the context of China’s new curriculum reform, research on big data in education is in the process of deepening. A series of high-tech information technologies represented by big data technology is widely used in education, thus promoting intelligent campus construction. Based on this, the researcher combed through the relevant literature and found that there is still much room for improvement in the current research on the visualization of education big data, especially the research on the design of education informatization platforms based on the visualization of education big data. This study first summarizes the current and practical use of educational visualization research and finds that big educational data generated in the educational process plays an essential role for students, teachers, and teaching administrators. Visualizing big educational data provides an optimized way for students, teachers, and teaching administrators. Therefore, this paper designs the framework design process, platform selection, related technology introduction, platform implementation, and functional analysis of the educational information platform visualized by big data and conducts system testing. Through the research of 250 customers, it is concluded that the platform satisfaction of educational information visualized by big data is 92.4%, the satisfaction of individual function is 95.6%, system ease of use is 93.2%, and the satisfaction of system compatibility is 93.6%.
Design and Analysis of Intelligent Robot Based on Internet of Things Technology
Authors: Yao Yunfeng , Li Suling
Published: May 2022

This research uses Auto-ID Labs radio frequency identification system to realize the information dissemination from the destination node to the nodes in its neighborhood. The purpose is to forward messages and explore typical applications. Realize the intelligent analysis and management of IoT devices and data. Design a set of edge video CDN system, in the G1 data set A = 9, p = 9, ℤp = 9, lℤp = 8, AES = 5, ES = 9. Distribute some hot content to public wireless hotspots closer to users in advance, A = 9, p = 7, ℤp = 9, lℤp = 9, AES = 9, ES = 8. At present, a large amount of research is mainly to deploy an edge node between the end node of the Internet of Things and the cloud computing center to provide high-quality services. By learning a stable dynamic system from human teaching to ensure the robustness of the controller to spatial disturbances. FPP-SCA plan FPP-SCA = 1.99, FPP-SCA = 1.86, FPP-SCA = 1.03, FPP-SCA = 1.18, FPP-SCA = 1.01, FPP-SCA = 1.46, FPP-SCA = 1.61.The more robots work in an unstructured environment, with different scenarios and tasks, the comparison shows that the FPP-SCA scheme is the optimal model F-S0 = 2.52, F-S5 = 2.38, F-S10 = 2.5, F- S15 = 2.09, F-S20 = 2.54, F-S25 = 2.8, F-S30 = 2.98.
Studies of Metal Organic Networks via M‐Polynomial‐Based Topological Indices
Authors: Hussain Muhammad Tanveer , Javaid Muhammad , Parveen Sajida , Awais Hafiz Muhammad , Alam Md Nur
Published: May 2022

Topological index (TI) is a graph‐theoretic tool that is used to study different physical and structural properties of the networks in various disciplines of science such as computer science, chemistry, and information technology. In this article, we study transition metal tetra‐cyano‐benzene organic networks by computing their M‐polynomials and various topological indices (TIs). At the end, a comparison is also included between all the computed degree‐based topological indices to show their betterness.
Evolving Machine Learning Methods for Density Estimation of Liquid Alkali Metals over the Wide Ranges
Authors: Lin Tao , Seraj Amir
Published: May 2022

Alkali metals are widely used as industrial materials in products such as electrochemical cells because of their properties that make them suited to high temperatures. In this study, three computational approaches including gene expression programming (GEP), least squares support vector machine (LSSVM), and adaptive neuro fuzzy inference system (ANFIS) have been suggested to estimate density of different liquid alkali metals in extensive ranges of pressure and temperature. An experimental databank involving 595 experimental alkali metals’ densities has been gathered to prepare and test the models. The mathematical and visual comparisons of these models’ outputs and real density values are used to assess capacities of GEP, LSSVM, and ANFIS models in prediction of alkali metals’ density. The determined R-squared values for GEP, LSSVM, and ANFIS are 0.9999, 1, and 1, respectively. The MSE values are estimated to be 0.9184, 0.815, and 0.154 for GEP, ANFIS, and LSSVM, respectively. According to these results, these models can be suggested as simple and accurate ways for determining alkali metals’ properties. Results showed that LSSVM has the best performance in comparison with GEP and ANFIS. Moreover, the parametric analysis of input parameters is carried out to show the impact of them on alkali metals’ density. According to this analysis, the amount of lithium can be the most effective parameter on the mixture density.
Simulation Study of the Velocity Profile and Deflection Rate of Non-Newtonian Fluids in the Bend Part of the Pipe
Authors: Wang Defeng , Cheng Zhanbo , Shi Qingwen , Zhao Jinhai , Mischo Helmut
Published: May 2022

As resource extraction moves deeper underground, backfill mining has received a lot of attention from the industry as a very promising mining method that can provide a safe workplace for workers. However, the safe and efficient transport of fill slurry through pipelines still needs more exploration, especially in the bend section. In order to investigate the flow characteristics and velocity evolution of the slurry in the bend section of the pipe, a three-dimensional (3D) pipe model was developed using the computational fluid dynamics software Fluent, and nine sets of two-factor, three-level simulations were performed. Furthermore, a single-factor analysis was presented to investigate the effects of the two main influencing factors on the shifting of the maximum velocity of the slurry towards the distal side in the bend section, respectively. Then, the response surface analysis method was applied to the two-factor analysis of the maximum velocity shift and the weights of the two influencing factors were specified.
An Intelligent Deep Learning Model for CO2 Adsorption Prediction
Authors: Mahmoud Hanan Ahmed Hosni , Hakami Nada Ali , Hafez Alaaeldin M.
Published: May 2022

In this paper, we propose a supervised deep learning neural network (D-CNN) approach to predict CO2 adsorption form the textural and compositional features of biomass porous carbon waste and adsorption features. Both the textural and compositional features of biomass porous carbon waste are utilized as inputs for the D-CNN architecture. A deep learning neural network (D-CNN) is proposed to predict the adsorption rate of [Formula: see text] on zeolites. The adsorbed amount will be classified and predicted by the D-CNN. Three tree machine learning models, namely, gradient decision model (GDM), scalable boosting tree model (SBT), and gradient variant decision tree model (GVD), were fused. A feature importance metric was proposed using feature permutation, and the effect of each feature on the target output variable was investigated. The important extracted features from the three employed model were fused and used as the fusion feature set in our proposed model: fusion matrix deep learning model (FMDL). A dataset of 1400 data items, on adsorbent type and various adsorption pressure, is used as inputs for the D-CNN model. Comparison of the proposed model is done against the three tree models, which utilizes a single training layer. The error measure of the D-CNN and the tree model architectures utilize the mean square error confirming the efficiency of 0.00003 for our model, 0.00062 for the SBT, 0.00091 for the GDM, and 0.00098 for the GVD, after 150 epochs. The produced weight matrix was able to predict the [Formula: see text] adsorption under diverse process settings with high accuracy of 96.4%.
WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer’s Disease
Authors: Taghavirashidizadeh Ali , Sharifi Fatemeh , Vahabi Seyed Amir , Hejazi Aslan , SaghabTorbati Mehrnaz , Mohammed Amin Salih
Published: May 2022

Alzheimer’s disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.
MDM2 and Prognosis
Authors: Onel Kenan , Cordon-Cardo Carlos
Published: May 2022

The cellular stress response pathway regulated by the p53 tumor suppressor is critical to the maintenance of genomic integrity and to the prevention of oncogenic transformation. Intracellular levels of p53 are tightly regulated by an autoregulatory feedback loop comprised of p53 and MDM2. It might be predicted that disruption of this loop, either through p53 mutation or overexpression of MDM2, would be a negative prognostic marker for cancer development, likelihood of relapse, or response to therapy. In fact, although MDM2 overexpression is common in cancer, it can be both a positive and a negative predictor of outcome in different tumors, and its significance as a biomarker remains controversial. Data from a number of different tumor types are reviewed for the predictive significance of MDM2 expression, along with evidence for different mechanisms of MDM2 overexpression in these different tumors.

In light of the biological complexities underlying the p53-MDM2 loop, it is, perhaps, not surprising that no simple paradigm exists that is generally applicable. Much work remains to be done to elucidate the basic mechanisms underlying the physical interactions between the two proteins, the role of protein modifications in altering those interactions, and also the genetic and transcriptional deregulations by which protein levels are altered in human cancers. Only in this way will truly biologically relevant predictive factors emerge.
Cyclin-Dependent Kinase 6 Inhibits Proliferation of Human Mammary Epithelial Cells
Authors: Lucas Joseph J. , Domenico Joanne , Gelfand Erwin W.
Published: May 2022

Many defects in cancer cells are in molecules regulating G1-phase cyclin-dependent kinases (cdks), which are responsible for modulating the activities of Rb family growth-suppressing proteins. Models for understanding how such defects affect proliferation assume that cdks are responsible for sequentially phosphorylating, and hence inactivating, the growth-suppressing functions of Rb family proteins, thus promoting cell cycle progression. However, cdks also play a role in formation of growth-suppressing forms of pRb family molecules, including the “hypophosphorylated” species of pRb itself. Here, it is shown that normal human mammary epithelial cells have a high amount of cdk6 protein and activity, but all breast tumor-derived cell lines analyzed had reduced levels, with several having little or no cdk6. Immunohistochemical studies showed reduced levels of cdk6 in breast tumor cells as compared with normal breast tissue in vivo. Cdk6 levels in two breast tumor cell lines were restored to those characteristic of normal human mammary epithelial cells by DNA transfection. The cells had a reduced growth rate compared with parental tumor cells; cells that lost ectopic expression of cdk6 reverted to the faster growth rate of parental cells. Cell lines with restored cdk6 levels accumulated higher amounts of the Rb family protein p130 as well as E2F4, a suppressing member of the E2F family of transcription factors, in their nuclei. The results suggest that cdk6 restrains rather than stimulates breast epithelial cell proliferation and that its loss or down-regulation could play a role in breast tumor development.
The Cytoskeleton Differentially Localizes the Early Growth Response Gene-1 Protein in Cancer and Benign Cells of the Prostate
Authors: Mora Gloria R. , Olivier Kenneth R. , Cheville John C. , Mitchell Richard F. , Lingle Wilma L. , Tindall Donald J.
Published: May 2022

Prostate cancer is the most prevalent malignancy and the second leading cause of cancer mortality in men. Early growth response gene-1 (EGR-1) plays a crucial role in the development and progression of prostate cancer. The presented data show that EGR-1 differs in cellular localization in benign cells compared with malignant prostate cells and that this localization is critical for the transcriptional activation of EGR-1-dependent genes. Immunohistochemistry of human prostate cancer specimens demonstrated higher levels of EGR-1 in malignant cells located predominantly in the cytoplasm, whereas benign cells contained lower levels of EGR-1 located predominantly in the nucleus. Benign prostate cells responded to mitogens in vitro, with increased levels of EGR-1, rapid nuclear translocation, and enhanced transcriptional activity, whereas malignant prostate cells did not exhibit the same responses, and the protein remained in the cytoplasm. The central aspect of this difference is the association of EGR-1 with microtubules, which is exclusive to the benign cells of the prostate and is requisite for the nuclear translocation and transcriptional activity of EGR-1. Our in vitro data demonstrate that the differences in EGR-1 between benign and malignant prostate cells extend beyond cellular levels, which was confirmed by immunohistochemistry in human tissues. Thus, we add the novel concept that microtubules regulate EGR-1 localization in benign prostate cells but not in malignant prostate cells.
Thombospondin-1 Disrupts Estrogen-Induced Endothelial Cell Proliferation and Migration and Its Expression Is Suppressed by Estradiol
Authors: Sengupta Krishanu , Banerjee Snigdha , Saxena Neela K. , Banerjee Sushanta K.
Published: May 2022

The natural hormone 17β-estradiol (17β-E2) is known to induce tumor angiogenesis in various target organs by activating positive regulators of angiogenesis. In this study, we show for the first time that in human umbilical vein endothelial cells (HUVECs), 17β-E2 transiently down-regulates the expression and secretion of a potent negative regulator of angiogenesis, thrombospondin-1 (TSP-1). This inhibitory effect of 17β-E2 is mediated through nongenomic estrogen receptor (ER)/mitogen-activated protein kinase (MAPK)/extracellular-regulated kinase (ERK) 1/2 and c-Jun NH2-terminal kinase (JNK)/stress-activated protein kinase (SAPK) signaling pathways, because this effect can be abolished by a pure ER antagonist (ICI 182,780) and inhibitors of downstream signaling proteins of MAPK signaling cascades, including MAPK kinase 1/2 and ERK1/2 inhibitor and JNK/SAPK inhibitor. To understand the functional role(s) of TSP-1 during estradiol-induced angiogenesis, we examined the growth and migration of endothelial cells in different experimental environments. Using a recombinant protein, we show that increments of TSP-1 protein concentration in culture medium significantly reduce the migration and proliferation of HUVECs stimulated by 17β-E2. Together, these studies suggest that TSP-1 can be considered an important negative factor in understanding the increased angiogenesis in response to estrogens.
Mitotic Aberration Coupled With Centrosome Amplification Is Induced by Hepatitis B Virus X Oncoprotein via the Ras-Mitogen-Activated Protein/Extracellular Signal-Regulated Kinase-Mitogen-Activated Protein Pathway
Authors: Yun Chawon , Cho Hyeseon , Kim Su-Jeong , Lee Jae-Ho , Park Sun Yi , Chan Gordon K. , Cho Hyeseong
Published: May 2022

Multinucleated cells have been noted in pathophysiological states of the liver including infection with hepatitis B virus (HBV), the status of which is also closely associated with genomic instability in liver cancer. Here, we showed that hepatitis B virus X oncoprotein (HBx) expression in Chang cells results in a multinuclear phenotype and an abnormal number of centrosomes (n  ≥ 3). Regulation of centrosome duplication in HBx-expressing ChangX-34 cells was defective and uncoupled from the cell cycle. HBx induced amplification of centrosomes, multipolar spindle formation, and chromosomal missegregation during mitosis and subsequently increased the generation of multinucleated cells and micronuclei formation. Treatment with PD98059, a mitogen-activated protein/extracellular signal-regulated kinase (MEK) 1/2 inhibitor, significantly reduced the number of cells with hyperamplified centrosomes and decreased the multinucleated cells and micronuclei formation. Consistently, the phospho-ERK level during cell progression was substantially higher in ChangX-34 cells than that of Chang cells. In contrast, neither wortmannin, an inhibitor of phosphoinositide-3 kinase, nor SB203589, an inhibitor of p38 mitogen-activated protein kinase (MAPK), showed any effects. Introduction of Ras dominant-negative (D/N) and MEK2 D/N genes into ChangX-34 cells significantly alleviated centrosome amplification, whereas introduction of the PKC D/N and PKB D/N genes did not. Thus, our results demonstrate that the HBx induced centrosome hyperamplification and mitotic aberration by activation of the Ras-MEK-MAPK. Intervention of this signaling pathway could suppress the centrosome amplification as well as mitotic aberration. These findings may provide a possible mechanism by which HBx promotes phenotypic progression by predisposing chromosomal alteration in HBV-infected liver.
Raf and RhoA Cooperate to Transform Intestinal Epithelial Cells and Induce Growth Resistance to Transforming Growth Factor β
Authors: Du Jianguo , Jiang Bo , Coffey Robert J. , Barnard John
Published: May 2022

Although unregulated activation of the Ras/Raf/mitogen-activated protein kinase kinase/Erk signaling pathway is believed to be a central mechanism by which many cell types undergo oncogenic transformation, recent studies indicate that activation of Raf kinase by oncogenic Ras is not sufficient to cause tumorigenic transformation in intestinal epithelial cells. Thus, identification of signaling proteins and pathways that interact with Raf to transform intestinal epithelial cells may be critical for understanding aberrant growth control in the intestinal epithelium. Functional interactions between Raf and the small GTPase RhoA were studied in RIE-1 cells overexpressing both activated Raf(22W) and activated RhoA(63L). Double transfectants were morphologically transformed, formed colonies in soft agar, grew in nude mice, overexpressed cyclin D1 and cyclooxygenase-2 (COX-2), and were resistant to growth inhibition by transforming growth factor (TGF) β. RIE-Raf and RIE-RhoA single transfectants showed none of these characteristics. Expression of a dominant-negative RhoA(N19) construct in RIE-Ras(12V) cells was associated with markedly reduced COX-2 mRNA, COX-2 protein, and prostaglandin E2 levels when compared with RIE-Ras(12V) cells transfected with vector alone. However, no change in transformed morphology, growth in soft agar, cyclin D1 expression, TGFα expression, or TGFβ sensitivity was observed. In summary, coexpression of activated Raf and RhoA induces transformation and TGFβ resistance in intestinal epithelial cells. Although blockade of RhoA signaling reverses certain well-described characteristics of RIE-Ras cells, it is insufficient to reverse the transformed phenotype and restore TGFβ sensitivity. Blockade of additional Rho family members or alternate Ras effector pathways may be necessary to fully reverse the Ras phenotype.
Multiple Aspects of the Phenotype of Mammary Epithelial Cells Transformed by Expression of Activated M-Ras Depend on an Autocrine Mechanism Mediated by Hepatocyte Growth Factor/Scatter Factor
Authors: Zhang Kai-Xin , Ward Katherine R. , Schrader John W.
Published: May 2022

Multiple aspects of the transformed phenotype induced in a murine mammary epithelial cell line scp-2 by expression of activated G22V M-Ras, including maintainance of cell number at low density, anchorage-independent growth, invasion of Matrigel, and secretion of matrix metalloproteinases (MMP) 2 and 9, were dependent on an autocrine mechanism. Conditioned medium from dense cultures of scp-2 cells expressing G22V M-Ras, but not from parental cells, induced activation of Erk and Akt in cells expressing G22V M-Ras, maintained the cell number and promoted anchorage-independent growth of cells expressing G22V M-Ras (although not the parental cells), and induced scattering of MDCK cells. The latter activities were blocked by neutralizing antibodies to hepatocyte growth factor/scatter factor (HGF/SF) and could be mimicked by HGF/SF. Anti-HGF/SF antibodies also inhibited invasion of Matrigel, and the production of MMP-2 and MMP-9, together with urokinase-type plasminogen activator, was secreted by G22V M-Ras scp-2 cells but not by parental cells. Invasion of Matrigel was blocked by an inhibitor of MMPs, BB94, and by the mitogen-activated protein kinase kinase 1/2 kinase inhibitor PD98059 but was only marginally affected by the phosphatidylinositol 3-kinase inhibitor LY294002. Autocrine HGF/SF was thus critical for expression of key features of the phenotype of mammary epithelial cells transformed by expression of activated M-Ras.