bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review of Privacy Decision-making Mechanisms in Online Social Networks
Authors: Alemany J. , Val E. Del , García-Fornes A.
Published: January 2022

Personal information of online social networks (OSNs) is governed by the privacy policies chosen by users besides OSN’s policies. Users make these decisions using privacy mechanisms, but privacy problems and regrets are daily reported. This article reviews current privacy mechanisms and solutions. For this, we analyze all the sub-decisions and elements of online communication involved in the privacy decision-making process. However, the differences in users’ motivations and the disclosure of too sensitive information (among others) can lead to loss of privacy. In this work, we identify requirements such as automation, preference-centered, relationship-based, and multi-party privacy mechanisms, which have been more researched. But also other requirements (recently emerged), such as privacy preservation with risk metrics, explainability, and ephemeral messages. We explore all the advances made in the literature, and we have seen that most of these have been focused on matching the users’ preferences with their decision (which is not appropriate, because users cannot evaluate all of the potential privacy scenarios) instead of assessing privacy risk metrics, adaptation, and explainability. Therefore, we have identified open challenges, such as metrics for assessing privacy risks, explainable solutions for users, ephemeral communication solutions, and the application of these requirements to the multi-party privacy scenario.
Assessment of Routing Attacks and Mitigation Techniques with RPL Control Messages: A Survey
Authors: Bang Ankur O. , Rao Udai Pratap , Kaliyar Pallavi , Conti Mauro
Published: January 2022

Routing Protocol for Low-Power and Lossy Networks (RPL) is a standard routing protocol for the Low Power and Lossy Networks (LLNs). It is a part of the IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) protocol stack. Features such as energy-efficient mechanisms and availability of the secure modes of operations make RPL suitable for the constrained Internet of Things (IoT) devices. Hence, the majority of IoT applications rely on RPL for data communication. However, routing security in RPL-based IoT networks is a significant concern, motivating us to study and analyze routing attacks and suggested countermeasures against them. To this end, we provide a comprehensive survey on the state-of-the-art security threats and their corresponding countermeasures in RPL-based IoT networks. Based on our study, we propose a novel classification scheme that uses a mapping between RPL attacks and their countermeasure techniques to the RPL control messages used to develop these techniques. Furthermore, we provide an in-depth statistical analysis that includes analysis of routing attacks through the RPL control messages, distribution of various mitigation techniques as per the method used, RPL control messages involved in the mitigation techniques, and details of the tools used by multiple researchers. In the end, we highlight some open challenges and future research opportunities on this topic. We believe that this survey will be beneficial to researchers and practitioners working in the area of RPL security.
Study of Ruthenium-Contamination Effect on Oxygen Reduction Activity of Platinum-Based PEMFC and DMFC Cathode Catalyst
Authors: Kaplan D. , Tereshchuk P. , Olewsky C. , Keinan L. , Ben-Yehuda O. , Shviro M. , Natan A. , Peled E.
Published: January 2022

We outline a systematic experimental and theoretical study on the influence of ruthenium contamination on the oxygen reduction activity (ORR) of a Pt/C catalyst at potentials relevant to a polymer electrolyte fuel cell cathode. A commercial Pt/C catalyst was contaminated by different amounts of ruthenium, equivalent to 0.15–4 monolayers. The resulting ruthenium-contaminated Pt/C powders were characterized by energy–dispersive x–ray spectroscopy (EDS), x–ray photoelectron spectroscopy (XPS) and scanning transmission electron microscopy (STEM) to verify ruthenium contamination. A rotating disk electrode (RDE) technique was used to study the influence of ruthenium on oxygen reduction kinetics. Density functional theory (DFT) calculations were performed to estimate the oxygen reduction activity of the platinum surface with increasing ruthenium coverage, simulating ruthenium-contaminated Pt/C. The binding energies of O and OH on the surfaces were used for activity estimations. It was found that the specific activity of the ORR at 0.85 V vs RHE exhibited an exponential-like decay with increased ruthenium contamination, decreasing by ∼45% already at 0.15 monolayer-equivalent contamination. The results of the DFT calculations were qualitatively in line with experimental findings, verifying the ability of the chosen approach to predict the effect of ruthenium contamination on ORR on platinum.
From the President: Making the Society Go
Authors: Hall Dale E.
Published: January 2022

In the spring of this year, as I prepared for my year as president of ECS, I called about 30 people and asked them to serve on the Society’s various committees. I must admit that I had conflicting feelings going into the process. On the one hand, I was asking colleagues and friends to donate their time and effort—and I’m aware that for many of us spare time has become almost an oxymoron. On the other hand, I was offering a group of Society members the opportunity to become involved in governing and operating an organization to which many of us feel a deep attachment and commitment.
Reusability of Photocatalytic CoFe2O4@ZnO Core–Shell Nanoparticles for Dye Degradation
Authors: Suharyadi Edi , Muzakki Afifah , Istiqomah Nurul Imani , Puspitarum Deska Lismawenning , Purnama Budi , Djuhana Dede
Published: January 2022

The reusability of CoFe2O4@ZnO core–shell nanoparticles (NPs) for the photocatalytic degradation of methylene blue (MB) under UV radiation was successfully investigated. CoFe2O4@ZnO NPs with various CoFe2O4–to–ZnO concentration ratios were synthesized as magnetic photocatalysts. The X-ray diffraction spectra showed that the NPs had a cubic spinel ferrite phase structure and a hexagonal wurtzite phase of ZnO. Fourier-transform infrared spectra showed the presence of Moct-O, Mtet-O, and Zn–O at 593, 347–389, and 410–429 cm−1, respectively. The CoFe2O4@ZnO NPs had a saturation magnetization of approximately 30 emu g−1 and a coercivity of approximately 280 Oe. The absorbance spectra showed that the absorbance peak of the CoFe2O4@ZnO NPs broadened and shifted to the right (higher wavelength) with increasing ZnO concentration. The CoFe2O4@ZnO NPs with higher ZnO concentrations exhibited higher photocatalytic activities and degradation rates. The enhancement of MB degradation can be attributed to the formation of an internal structure between CoFe2O4 and ZnO. The degradation rate of CoFe2O4@ZnO decreased slightly after each successive recycle. The results indicated that the recycled CoFe2O4@ZnO NPs could be reused three times for photocatalytic degradation. As there is no significant decrease in the photocatalytic degradation after four successive recycles, the CoFe2O4@ZnO NPs are suitable for application in dye degradation.
Optical Characteristics of Cadmium Oxide and Magnesium Doped with Nanoclusters
Authors: Othman Mazin
Published: January 2022

The optical characteristics of cadmium oxide and Magnesium (Mg) doped with nanocluster structures were studied using a computational simulation method based on density functional theory. The CASTEP package was used to make the calculations using the local density approximation. The optical bandgap energy increased from 1.544 to 2.896 eV with Mg doping increased. Furthermore, optical results showed that reflectivity, absorption and conductivity of the cluster system structures varied from (400–800 nm) range with doping.
Excited State Properties of Layered Two-Dimensional MSi2N4 (M = Mo, Cr, and W) Materials from First-Principles Calculations
Authors: Ding Wangyang , Xue Zhouhong , Li Jiayu , Li Mingyang , Bai Liang , Zhou Qi , Zhou Xin , Peng Ying , Miao Lei
Published: January 2022

Two-dimensional (2D) MoSi2N4 layered material successfully prepared in the experiment was shown to have good light absorption performance, although its internal mechanism is not clear. We discuss the effects of replacing Mo with W and Cr atoms, respectively, on the light absorption performance of a 2D MoSi2N4 monolayer through first-principles calculations. M-site atoms play a major role in the accumulation of electrons in MA2Z4 system materials. In the visible light region, as the excited state is higher, the number of holes that appear on the N atom layer next to the central layer decreases, whereas those appearing on the N atoms of the outermost layer at both ends increase in number, indicating the research value of Z-site atoms in MA2Z4 system. More importantly, the electrons of Si atoms in the sub-outer layer will transfer when the excitation energy is sufficient, and the Mo atoms in the center layer will obtain electrons not only from other atoms but also from themselves. Replacing Mo atoms in the monolayer of MoSi2N4 with Cr atoms can redshift the ultraviolet-visible spectrum (UV) and reduce the height of the strongest absorption peak. Simultaneously, replacing MoSi2N4 monolayer with W atoms causes the blue shift of the ultraviolet-visible spectrum.
Effect of La Doping on NiCr2O4 Properties and Catalytic Applications toward Decolorization of 4-Nitrophenol
Authors: Rajeswari G. , Prabavathi N. , Prakasam A. , kumar Rajneesh , Geetha S. , Tamizhdurai P. , ALOthman Zeid A. , Aljuwayid Ahmed muteb , Rajaji U.
Published: January 2022

We report the successful synthesis of urea as a fuel for low-temperature microwave combustion-based synthesis of NiCr2O4 and La-doped NiCr2O4. Synthesis parameters include the kind of precursor, fuel, and container, as well as how external heat is provided to achieve the combustion reaction. Crystal structures, morphology, surface area, solar cells, Fourier-transform infrared spectrometry, and catalytic activity were used to characterize the products. No impurities phase formed in the as-synthesized NiCr2O4 sample, confirmed by XRD. Surface area was measured with a Brunauer–Emmet–Teller adsorption isotherm. Schottky and Frenkel formation energies have been investigated. This demonstrates the time needed for full decolorization of 4-Nitrophenol (yellow color) by NiCr2O4 and La-doped NiCr2O4. The La-doped NiCr2O4 (0.03%) showed the greatest catalytic activity compared to pristine and other La-doped NiCr2O4. The decolorization occurs in 10 min. The chemical hardness and surface area of the chromites appear to be important in determining catalytic performance. For multi oxide systems, the counter-doped ions in the A and B sites may alter the metal ion-oxide bond, promoting this partial oxidation process at the interface. The results suggest multiple research subjects focusing on such materials for the future.
Tin Alloy Nanoparticles for Selective Electrocatalytic Reduction of Carbon Dioxide to Formate
Authors: Camilo Mariana R. , Lima Fabio H. B.
Published: January 2022

The selective electrocatalytic reduction of CO2 to formate ions or to formic acid is attracting the attention of several researchers. This is mainly because these products can be utilized as hydrogen carriers and as fuels for direct or for indirect fuel cells. In aqueous media, electrocatalysts formed by metals with high hydrogen evolution overpotential, such as Pd, Hg, In, and Sn, are selective for the production of formate (or formic acid, depending on the electrolyte pH). Among these metals, Sn has gained notice because it is abundant and relatively nontoxic. The reasons for the selectivity observed on Sn electrodes are still in debate and has been the subject of recent papers [1,2]. It seems that tin allows the carbon dioxide reduction intermediate to be adsorbed via the oxygen atoms after a proton-couple electron transfer step, so conducting the reaction to the production of formate. In addition, the studies have been discussed the effect of the surface hydroxides, which may enhance the CO2 reduction mainly via two effects: (i) suppressing or decelerating the water electro-reduction and; (ii) allowing a first chemical reaction step with CO2, so producing an adsorbed bicarbonate intermediate (via oxygen atoms), so facilitating the protonation of the carbon atom. In this paper, we will present some results obtained for the CO2 electrochemical reduction on nanostructured tin-alloys, such as Sn-Pd and Sn-Co. The faradaic efficiencies for formate (or formic acid) production were determined via quantitative determination by cyclic voltammetry and experiments of on-line Differential Electrochemical Mass Spectrometry (DEMS) for the formate electro-oxidation after experiments of CO2 electrolysis. It will be discussed the correlation between the nanostructure morphology and Sn oxidation state with the stability and faradaic efficiency for the production of formate (or formic acid) on different electrolytes and pHs. We expect that the obtained results will serve to further understand the main electrocatalyst features that control the selectivity and stability for the CO2 reduction to formate.

[1] J.T. Feaster, C. Shi, E.R. Cave, T. Hatsukade, D.N. Abram, K.P. Kuhl, C. Hahn, J.K. Norskov, T.F. Jaramillo, ACS Catal. 7 (2017) 4822 – 4827.

[2] J.E. Pander, M.F. Baruch, A.B. Bocarsly, ACS Catal. 2016, 6, 7824−7833.

Acknowledgements

The authors gratefully acknowledge financial support from FAPESP (2016/13323-0 and 2013/16930-7) and CNPq (306469/20162).
(Invited) Ligand-Directed CO2 Conversion at Bimetallic Au/Cu Nanocatalysts
Authors: Kauffman Douglas R. , Alfonso Dominic R
Published: January 2022

The electrochemical CO2 reduction reaction (CO2RR) is a promising approach for converting waste CO2 emissions into value-added chemicals and fuels. Product selectivity will be a key requirement because down-stream chemical separation steps will introduce cost barriers and reduce the efficiency of large-scale systems. Gold electrocatalysts selectively convert CO2 into CO, which can be further processed into a wide range of other commodity chemicals, industrial precursors, and fuel additives. Copper is another popular material because it is less expensive and can directly produce hydrocarbons, but it typically demonstrates a wider product distribution than gold. Bimetallic gold-copper electrocatalysts have emerged as promising candidates for selective CO2 conversion with lower precious metal loadings, but most Au/Cu systems reported to date demonstrate lower product selectivity than pure gold.

Here, we’ve combined experiment and theory to describe the CO2RR performance of monodisperse (~1.5 nm), thiol-capped, gold-copper nanoparticles (Au/Cu NPs) containing between 0-64% copper. NPs containing 49% Cu demonstrated the best performance with 94±5% CO Faradaic efficiency between -0.5V to -1.0V vs. RHE and turnover frequencies reaching 60 molecules site-1 s-1. This activity represents a 4-8 fold improvement in catalytic activity and improved product selectivity compared with identically sized, thiol capped Au NPs. X-ray absorption spectroscopy and extended x-ray absorption fine structure (EXAFS) analysis characterized the Au/Cu NP electronic structure and identified unique copper-thiol structures at the NP surface. Complementary density functional theory modeling with realistic, thiol-capped Au/Cu NP structures identified key relationships between NP composition and electrocatalytic performance. Our results show that unique copper-thiol surface structures on the Au/Cu NPs influenced CO2RR performance by stabilizing bound *CO reaction intermediates and preventing H2 evolution. Experiment and theory both indicated less-selective CO2RR at ligand-free NPs, and ligand-free Au/Cu NPs produced H2 with approximately 95% selectivity. Our combination of experiment and theory demonstrate that ligand-directed surface structuring can influence NP chemistry, improve CO2RR product selectivity, and produce highly active electrocatalysts with reduced precious metal loadings.
Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices
Authors: Kaur Damandeep , Singh Surender , Mansoor Wathiq , Kumar Yogesh , Verma Sahil , Dash Sonali , Koul Apeksha
Published: January 2022

The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is likely the most severe ailment that necessitates early discovery and treatment, and it requires the competence of neurosubject-matter experts and radiologists. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine and deep learning techniques are being increasingly commonly applied throughout healthcare industries. The current study depicts the methodologies and procedures used to detect a tumor inside the brain utilizing machine and deep learning techniques. Initially, data were preprocessed using contrast limited adaptive histogram equalization. Then, features were extracted using principal component analysis and independent component analysis (ICA). Next, the image was smoothed using multiple optimization techniques such as firefly and cuckoo search, lion, and bat optimization. Finally, Naïve Bayes and recurrent neural networks were utilized to classify the improved results. According to the findings, the ICA with cuckoo search and Naïve Bayes has the best mean square error rate of 1.02. With 64.81% peak signal-to-noise and 98.61% accuracy, ICA with hybrid optimization and a recurrent neural network (RNN) proved to better than the other algorithms. Furthermore, a Smartphone application is designed to perform quick and decisive actions. It helps neurologists and patients identify the tumor from a brain image in the early stages.
Comprehensive Analysis of Pyroptosis-Related Long Noncoding RNA Immune Infiltration and Prediction of Prognosis in Patients with Colon Cancer
Authors: Liu Li , Chen Wenzheng , Li Yebei , Fu Pengcheng , Cao Yi , Li Zhengrong , Xiong Jianbo , Jie Zhigang
Published: January 2022

Colon cancer (CC) is one of the most prevalent malignant tumours of the alimentary canal. It is unclear whether pyroptosis-related lncRNA expression is correlated with CC prognosis. We discovered 20 pyroptosis-related lncRNAs that were expressed differently in CC and normal colon tissues in our investigation. Based on differentially expressed genes (DEGs), we grouped all CC patients into two categories (Clusters 1 and 2). Cluster 1 was shown to be connected with a higher overall survival rate, upregulated expression of immune checkpoints, higher immunoscores, higher estimated scores, and immune cell infiltration. Using data from the Cancer Genome Atlas (TCGA), to create a multigene signature, the predictive significance of each lncRNA linked with pyroptosis for survival was assessed. A 9-lncRNA signature was established using the least absolute shrinkage and selection operator (LASSO) Cox regression method, and all CC patients in the TCGA cohort were classified into low-risk or high-risk groups. The low-risk CC patients had a much greater chance of survival than those in the high-risk group. The risk score is an independent prognostic indicator for predicting survival. In addition, risk characteristics are linked to immune characteristics. In summary, pyroptosis-related lncRNAs can be used to predict CC prognosis and participate in tumour immunity.
Parameter Optimization Study of Gas Hydrate Reservoir Development Based on a Surrogate Model Assisted Particle Swarm Algorithm
Authors: Zhang Le , Huang Xin , He Jiayuan , Cen Xueqi , Liu Yongge
Published: January 2022

Using surrogate model to assist parameter optimization of oil and gas reservoir development can greatly reduce the call times of numerical simulator and accelerate the optimization process. However, for serial simulators or parallel simulators with low speedup ratio, the conventional method is still time-consuming. Firstly, an improved surrogate model assisted particle swarm optimization (PSO) algorithm was proposed in this paper. Then, the performance of the algorithm was analyzed using the Rastrigin function. Finally, the key operation parameters of a gas hydrate reservoir by depressurization−to−hot−water−flooding method were optimized with the new method. The results show that the new method only affects the update of the global optimal particle without interfering with the calculation process of the local optimal particles at the early stage of optimization. It realizes the rapid addition of the particle samples through the good parallel features of the PSO algorithm, and therefore, improve the precision of surrogate model in a short time. At the late stage of optimization, it is transformed into a local surrogate model to achieve rapid convergence, when the training time of the surrogate model exceeds the calculation time of the simulator. Both the optimization of Rastrigin function and operation parameters of gas hydrate development reveal that the new algorithm greatly reduces the number of iterations under the same accuracy and thus successfully accelerates the optimization process.
Numerical Simulation of Time Fractional BBM-Burger Equation Using Cubic B-Spline Functions
Authors: Kamran Mohsin , Abbas Muhammad , Majeed Abdul , Emadifar Homan , Nazir Tahir
Published: January 2022

The unidirectional propagation of long waves in certain nonlinear dispersive system is explained by the Benjamin-Bona-Mahony-Burger (BBM-Burger) equation. The purpose of this study is to investigate the BBM-Burger equation numerically using Caputo derivative and B-spline basis functions. The fractional derivative is considered in Caputo form, and

L
1

formula is used for discretization of temporal derivative. The interpolation of space derivative is done with the help of B-spline functions. The effect of

α

and time on solution profile of travelling wave for different domain of

x

is discussed in this paper. The numerical results have been presented to show that the cubic B-spline method is effective and efficient in solving the time fractional Benjamin-Bona-Mahony-Burger (BBM-Burger) equation. Moreover, the convergence and stability of the proposed scheme are analyzed. The error norms are also calculated to check the accuracy of the proposed scheme. The numerical results reflect that the proposed scheme can be used for linear and highly nonlinear models.
Device for Simulating Fluid Microgravity Environment Based on Magnetic Compensation Method and Research on Magnetic Fluid Lubrication Performance of Oil Film Bearing
Authors: Peng Han , Shangguan Linjian , Zhang Hai
Published: January 2022

At present, with the rapid development of the material market, the requirements of high performance and high precision of materials are increasingly exposed. Nanomagnetic fluid materials are more and more widely used in oil film bearings, but their compressive strength and performance are insufficient, which is difficult to meet the current requirements of material chemical properties. First of all, a device for simulating a microgravity environment with magnetic compensation is fabricated. Then, in the microgravity environment, according to the different proportions of magnetic solid particles, the base carrier liquid and the surfactant are mixed to produce nanomagnetic fluid and the nanomagnetic fluid with different composition proportions is prepared by adjusting the proportion of ferrous and ferric ions. Finally, the lubrication performance of oil film-bearing magnetic fluid with different composition ratios was tested. The results show that when the ratio of Fe2+ to Fe3+ is between 11 : 20 and 13 : 20, the MHD (magnetohydrodynamics) lubrication performance of oil film bearing is in the peak region. When the ratio is 3 : 5, the best lubrication performance can be achieved. When it is slightly higher than this ratio, the oxidation rate is accelerated due to more ferrous ions. Although it can have a good lubrication effect, it is easy to cause the overall fluidity of oil film-bearing magnetic fluid to deteriorate. Therefore, the oil film bearing nano-MHD with the ratio of 3 : 5 ferrous to ferric has the best lubrication performance in the microgravity environment simulated by magnetic compensation. Experiments have shown that when the magnetic fluid is heated in a magnetic field, the temperature gradient will cause the magnetization to change, which makes the magnetic force experienced by the liquid in each part different, causing convection. Therefore, by selecting the direction of the magnetic field and the heating surface, by applying an external magnetic field or promoting convection, or suppressing convection, convection in the opposite direction to the natural convection of gravity can also be realized. Moreover, magnetism can immediately promote high-temperature boiling, generate bubbles, and eliminate the generation of bubbles to promote heat transfer. With the above effects, the heat conduction between the heating wall and the liquid can be controlled, and its practical and potential application fields are very wide.
Research on Flipped Classroom of University Curriculum Using Eye Movement Analysis and LSTM Neural Network
Authors: Xi Jinjing
Published: January 2022

By reversing the arrangement of knowledge transfer and knowledge internalisation, FC (flipped classroom) changed the roles of teachers and students in traditional teaching and replanned the use of classroom time, realizing the innovation of traditional teaching mode. This research uses a network teaching platform to create a network platform supporting environment suitable for FC teaching in universities, allowing for the reform of university classroom teaching modes. An LSTM (Long Short-Term Memory) eye movement analysis algorithm is proposed to solve this problem. By analyzing the sequence of eye movements, this algorithm can predict students’ learning behavior and complete the current learning state detection. The research shows that the design and application of this research have achieved a good teaching effect in practice, which provides an idea for flipped classroom teaching design and curriculum resource design and development.
New Stabilization Properties of Pendulum Models Applying a Large Parameter
Authors: Ismail A. I. , Ghulman Hamza A.
Published: January 2022

In the present paper, we introduce new models of pendulum motions for two cases: the first model consists of a pendulum with mass M moving at the end of a string with a suspended point moving on an ellipse and the second one consists of a pendulum with mass M moving at the end of a spring with a suspended point on an ellipse. In both models, we use the Lagrangian functions for deriving the equations of motions. The derived equations are reduced to a quasilinear system of the second order. We use a new mathematical technique named a large parameter method for solving both models’ systems. The analytical solutions are obtained in terms of the generalized coordinates. We use the numerical techniques represented by the fourth‐order Runge–Kutta method to solve the autonomous system for both cases. The stabilities of the obtained solutions are studied using the phase diagram procedure. The obtained numerical solutions and analytical ones are compared to examine the accuracy of the mathematical and numerical techniques. The large parameter technique gives us the advantage to obtain the solutions at infinity in opposite with the famous Poincare’s (small parameters) method which was used by many outstanding scientists in the last two centuries.
Tourism Demand Forecast Based on Adaptive Neural Network Technology in Business Intelligence
Authors: Wang Liangliang
Published: January 2022

In order to improve the effect of tourism demand forecast, the commercial development of the tourism industry, and the actual experience of users, this paper uses adaptive neural network technology to conduct tourism demand forecast analysis. Moreover, this paper improves the adaptive neural network algorithm so that it can handle multiple data for tourism demand forecast. After improving the algorithm, this paper employs the actual process of tourism demand forecast to construct a tourism demand forecast model based on adaptive neural network technology. After that, this paper combines travel time and space data analysis to determine the system’s functional structure and network topology. Through experimental research, it can be seen that the tourism demand forecast model based on adaptive neural network technology proposed in this paper performs well in tourism demand forecast and meets the actual demand of modern tourism forecast.
Overexpression of miR-126 Protects Hypoxic-Reoxygenation-Exposed HUVEC Cellular Injury through Regulating LRP6 Expression
Authors: Sheikh Md Sayed Ali , Almaeen A. , Alduraywish A. , Alomair Basil Mohammed , Salma Umme , Fei Li , Yang T. L.
Published: January 2022

The aim of the study was to explore the clinical impact of circulatory miR-126 as a candidate for novel biomarker in patients with coronary artery disease (CAD) and its protective role against hypoxia/reoxygenation- (H/R-) exposed HUVEC cellular injury. A total of 278 subjects, which included 153 subjects with angiographically confirmed CAD, 70 unstable angina subjects, and 55 healthy individuals, along with 18-hour HR-induced HUVECs were recruited in this study. Plasma miR-126 levels were significantly downregulated in stable and unstable CAD patients as well as 18-hour HR-exposed HUVECs as compared with controls. Stable and unstable CAD subjects were significantly differentiated from healthy individuals with a predictive value of AUC 0.903 and 0.923, respectively. Moreover, peripheral circulatory miR-126 expressions in elderly (71-90 years) stable and unstable CAD patients were comparatively lower than younger (30-50 years) subjects. The caspase-3 activity, intracellular ROS concentrations, and cellular viabilities were evidently increased in 18-hour HR-exposed HUVECs than in normal cells (

P
<
0.001

). On the contrary, mimic expressions of miR-126 prominently reduced caspase-3 activity and intracellular ROS levels and markedly enhanced HUVEC cellular viabilities (

P
<
0.001

). LRP6 expressions were significantly elevated in HR-induced HUVECs, whereas overexpression of miR-126 remarkably decreased LRP6 expressions (

P
<
0.001

). Plasma miR-126 could be used as a novel biomarker for early prediction of CAD subjects. Overexpression of miR-126 significantly improved HUVEC cellular viabilities by downregulation of LRP6 protein expression, suggesting a potential therapeutic target for CAD patients.
Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption
Authors: Hamza Manar Ahmed , Althobaiti Maha M. , Al-Wesabi Fahd N. , Alabdan Rana , Mahgoub Hany , Hilal Anwer Mustafa , Motwakel Abdelwahed , Al Duhayyim Mesfer
Published: January 2022

Antibiotics have received a lot of attention as promising contaminants because of their ecotoxicological and long-term chemical stability in the atmosphere. Antibiotic adsorption on carbon-based materials (CBMs) such as charcoal and activated carbon has been identified as mainly effective for treating the wastewater strategies. Machine learning (ML) approaches were used to create generalized computation methods for tetracycline (TC) and sulfamethoxazole (SMX) adsorption in CBMs in this investigation. In the existing system, random forest and ANN methods were used for TC and SMX for predicting the quantities of antibiotics in the CBMs. For reducing the antibiotics from the industrial wastewater, the broadcast efforts of the experiments are a little complicated. In the proposed method, Gaussian process regression (GPR), active learning (AL), and ANN are used for predicting the antibiotic levels in the industrial wastewater. Below a variety of environmental parameters (e.g., warmth, solution pH) and adsorbent varieties, the created Ml algorithms outperformed classic isotherm models in conditions of generalisation. To evaluate TC and SMX adsorption on CBMs, we used comparative significance investigation and partial trust plots based on ML models. The proposed GPR reduces the antibiotics in wastewater; minimal experimental screening and the comparative significance and partial trust plot help in the treatment of wastewater.
Analysis of Cardiovascular Disease Angiography Process Based on Rough Set and Internet of Things
Authors: Gui Yuesheng , Qiu Jiawei , Wang Guangming
Published: January 2022

The angiography image enhancement technology has the potential to enhance the vascular structure in the image while suppressing the background and nonvascular structures simultaneously. This technology has the ability to enhance the result as close to the real structure of blood vessels as possible. Angiographic image processing is one of the essential contents in the field of medical image processing and analysis. However, the existing cardiovascular angiography schemes suffer from various issues. In this paper, the detection process of cardiovascular angiography is studied by combining the Internet of Things and rough set technology. Firstly, this paper designs the architecture design of the cardiovascular angiography process combined with the Internet of Things technology. Secondly, this paper uses a rough set algorithm to optimize the background noise and boundary shrinkage because of the sensitivity of the contrast background noise and boundary shrinkage. Simulation results verified the applicability and efficiency of the proposed model in the cardiovascular angiography scheme. The model has been optimized during implementation. Compared with the traditional algorithm, the same image data processing speed is significantly improved to ensure the enhancement effect.
Broken-Edge Decision-Making Strategy for COVID-19 over Air Railway Composite Network
Authors: Sun Hui , Qin Yicong , Mu Zhicheng , Wang Rui
Published: January 2022

In order to control the spread of the COVID-19 virus, this study proposes an ARCN-SUTS (air railway composite network susceptible-untested-tested-susceptible) model based on the correlation characteristics of the air railway composite network in mainland China. Furthermore, this study also puts forward a broken-edge decision-making strategy for the purpose of making decision about the edge efficiently broken and avoiding the second outbreak of the virus spread to minimize the economic losses for railway and civil aviation companies. Finally, simulation results demonstrate that the proposed strategy can effectively control the spread of the virus with minimal economic losses.
A Modified Anastomosis Technique for Esophagojejunostomy after Laparoscopy-Assisted Total Gastrectomy: A Single Team Preliminary Experience
Authors: Wu Zehui , Wang Bing , Liu Gang , Lu Jiaju , Zhang Chengxiong , Chen Fangzheng , Shi Lianghui , Xu Aman
Published: January 2022

Background and objectives. Laparoscopic total gastrectomy has developed rapidly in recent years. End-to-side Roux-en-Y esophagojejunostomy by using a circular stapler is a widely accepted reconstruction method, with low rates of anastomotic complications. However, although seldom reported, difficulty of stapler insertion sometimes occurs during operation because of the small diameter of the jejunum, and that will increase the risk of anastomotic leakage or stenosis. Herein, we describe a modified technique for esophagojejunostomy which can make the insertion of the stapler easier. Patients and methods. In this retrospective study, 112 patients who had undergone laparoscopiy-assisted total gastrectomy (LATG) with D2 lymphadenectomy were examined between January 2016 and December 2020. All of these operations were performed by the same surgeon team. Of these, 44 received a conventional Roux-en-Y end-to-side esophagojejunostomy (cRY) group, and the remaining 68 underwent pant-shaped Roux-en-Y esophagojejunostomy (pRY). Clinicopathological characteristics, surgical outcomes, and postoperative complications were compared between the groups. Results. There were no significant differences between the cRY group and pRY group regarding age, sex, BMI, neoadjuvant therapy, preoperative comorbidities, history of laparotomy, ASA score, tumor location, pathological stage, total operative time, incision length, blood loss, time-to-first flatus, time-to-first soft diet, and postoperative hospital stays. The proportions of patients who received a 21 mm stapler were higher in the cRY group (7/44) than that in the pRY group (0/68) (

P
=
0.003

). 7 anastomotic complications were reported (6 in the cRY group versus 1 in pRY group;

P
=
0.028

) of which five (83.3%) in the cRY were anastomotic stenosis versus none in the pRY group (

P
=
0.044

). Conclusions. The application of pant-shaped anastomosis for esophagojejunostomy after LTG is a safe and feasible procedure and has an advantage when the jejunum diameter is small.
A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets
Authors: Song Xiyu , Zeng Ying , Tong Li , Shu Jun , Yang Qiang , Kou Jian , Sun Minghua , Yan Bin
Published: January 2022

The superiority of collaborative brain-computer interface (cBCI) in performance enhancement makes it an effective way to break through the performance bottleneck of the BCI-based dynamic visual target detection. However, the existing cBCIs focus on multi-mind information fusion with a static and unidirectional mode, lacking the information interaction and learning guidance among multiple agents. Here, we propose a novel cBCI framework to enhance the group detection performance of dynamic visual targets. Specifically, a mutual learning domain adaptation network (MLDANet) with information interaction, dynamic learning, and individual transferring abilities is developed as the core of the cBCI framework. MLDANet takes P3-sSDA network as individual network unit, introduces mutual learning strategy, and establishes a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level. The results indicate that the proposed MLDANet-cBCI framework can achieve the best group detection performance, and the mutual learning strategy can improve the detection ability of individual networks. In MLDANet-cBCI, the F1 scores of collaborative detection and individual network are 0.12 and 0.19 higher than those in the multi-classifier cBCI, respectively, when three minds collaborate. Thus, the proposed framework breaks through the traditional multi-mind collaborative mode and exhibits a superior group detection performance of dynamic visual targets, which is also of great significance for the practical application of multi-mind collaboration.
Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM)
Authors: Wang Lei , Li Jinghang , Zhang Wenbo , Li Yu
Published: January 2022

In order to further accurately predict gas emission of working face, this paper proposes a prediction model of gas emission of working face based on the combination of improved artificial bee colony algorithm and weighted least squares support vector machine (IABC-WLSSAVM). The research steps are as follows: Firstly, in order to obtain the sparse solution of LSSVM, a more reliable prediction model is realized by weighting the error value. Secondly, the chaotic sequence is introduced into the artificial bee colony algorithm to find a better initial honey source, which increases the diversity of the population, and combines the Levy flight to update the search step to avoid falling into the trap of local optimum. At the same time, the improved artificial bee colony algorithm is used to optimize the kernel width

σ

and regularization parameter

λ

of WLSSVM, which improves the prediction accuracy and convergence rate of WLSSVM. Finally, the quantitative analysis model of WLSSVM is reconstructed by using the optimized parameters, and the nine parameters of buried depth of coal seam, gas content of coal seam, coal thickness, interlayer lithology, production rate of working face, length of working face, inclination of coal seam, gas content of adjacent layer, and thickness of adjacent layer are used as the main influencing factors. After normalization, the nonlinear prediction model of gas emission is established. The simulation results based on the three indicators of determination coefficient, root mean square error, and average relative variance show that the IABC-WLSSVM prediction model proposed in this paper can not only overcome the local optimization to obtain the global optimal solution but also has faster convergence speed and higher prediction accuracy. This prediction model has obvious advantages compared with the other three improved prediction models in terms of fitting, accuracy, and generalization ability, which can provide a reliable theoretical basis for the prediction of gas emission in coal mining face under complex factors and propose a new idea for the application of artificial intelligence in the construction of intelligent mines. At the same time, the prediction model can also be applied to other fields.
Finite Groups Which Are the Union of Autocentralizers of Some Automorphisms
Authors: Alamshahi Esmat , Moghaddam Mohammad Reza R. , Rostamyari Mohammad Amin
Published: January 2022

Let α be an automorphism of a given group G; then, CG(α) = {g ∈ G|α(g) = g} is called the autocentralizer of α in G. In this work, we study finite groups G, which can be written as the union of autocentralizers of some automorphisms of G. In particular, if a group G is 5‐A‐centralizers, then we determine the absolute central quotient G/L(G) of G, where A = Aut(G). Finally, it is shown that autocommutative property of a group is equivalent to the one, in which every A‐centralizer of its nontrivial elements is abelian.
[Retracted] Path Optimization of Enterprise Network Innovation Performance Management Based on Deep Learning and Internet of Things
Authors: Xiong Peiran
Published: January 2022

In today's global competition environment with rapid changes in market and technology, it is more and more difficult for enterprises to fully grasp the latest knowledge and learn all the technologies by relying on their own strength. It is particularly important for enterprises to establish a network relationship of a certain intensity with other external entities (upstream and downstream enterprises, peer enterprises, scientific research institutions, government departments, financial institutions, and other organizations) for their technological learning and improvement of technological innovation performance. From different perspectives, the academic circles have confirmed that the strength of network relationship does have an impact on the technological innovation performance of enterprises. This paper will explore the measurement scale of technology learning cost, and from the basic perspective of technology learning cost, deeply explore and analyze how enterprise technology innovation performance is affected by the strength of enterprise network relationship. This paper argues that the strength of network relationship can affect the cost of technology learning and therefore the performance of technology innovation. The main contents of this study include the following. (1) Through the collation and review of the related theories of network relationship strength, technological learning cost and technological innovation performance, and the existing research results, the theoretical model of this study is established, and the theoretical assumptions of this study are put forward. (2) A presurvey is carried out first, and the data collected from the presurvey are used to test the reliability and validity of the scale of this study, and the appropriate measurement scales for the strength of network relationship, technology learning cost, and technology innovation performance are determined. (3) Adopt the method of regression analysis combined with the SME method, to verify this paper builds the theoretical model, and further clarify the network relation intensity, technology acquisition cost, and cost of technological learning all dimensions and performance of technological innovation and network strength, technological learning mechanism between cost, and performance of technological innovation.
Clonidine Use for the Treatment of Nightmares in Posttraumatic Stress Disorder
Authors: Bange Jill S. , Melvin Kelly E.
Published: January 2022

Introduction. While clonidine is used clinically for the treatment of nightmares in posttraumatic stress disorder (PTSD), few case reports demonstrating this indication exist, and there have been few studies investigating clonidine’s mechanism of action for controlling nightmare symptoms. Case Report. In order to further characterize clonidine’s role in treating nightmare symptoms in PTSD, we offer this case report describing one United States veteran who presented to an inpatient psychiatric unit after a suicide attempt. At that time, she described a remote history of PTSD symptoms, including nightmares, flashbacks, hyperarousal, and avoidance behaviors which had been well controlled on sertraline and clonidine. Upon her admission, her home sertraline and alprazolam were continued but her home clonidine was not continued. On day two of her hospital stay, she stated that her nightmares had returned. Her home clonidine was restarted on day two. On day three and thereafter, the patient no longer complained of nightmares. Conclusion. Our patient’s nightmare symptoms had been controlled for years after beginning clonidine as an outpatient, but off clonidine, she had a return of her nightmare symptoms. Her nightmares again resolved once clonidine was resumed. Given this pattern in the patient’s response to clonidine, this case may serve as additional evidence in the literature that clonidine has a role in treating nightmares in PTSD. Current proposed mechanisms of action for clonidine’s ability to control nightmare symptoms in PTSD include that clonidine may alter the proportions of REM and non-REM sleep in a dose-dependent manner or that clonidine may play a role in memory consolidation. Further formal medication trials are the ideal future direction for establishing this role for clonidine.
Integrated Bioinformatic Analysis Identifies TIPIN as a Prognostic Biomarker in Hepatocellular Carcinoma
Authors: Chen Hui , Zhang Chunting , Zhou Qianmei , Guo Yanan , Ren Zhigang , Yu Zujiang
Published: January 2022

Background. Gene expression and DNA methylation analyses have long been used to identify cancer markers. However, a combination analysis of the gene expression and DNA methylation has yet to be performed to identify potential biomarkers of hepatocellular carcinoma (HCC). Methods. By matching gene expression profiles and promoter methylation data in The Cancer Genome Atlas (TCGA), genes with discrepant expression as well as genes with differential promoter methylation were identified. High-expression genes with low promoter methylation were defined as epigenetically induced (EI), while low-expression genes with high promoter methylation were defined as epigenetically suppressed (ES). The human protein interaction network was further integrated to construct the EI/ES gene interaction network, and the key genes in the subnet were identified as potential HCC biomarkers. The expression differences and prognostic values were verified in TCGA and Gene Expression Omnibus (GEO) databases, as well as with tissue chip technology. Results. Four key genes were identified: TIPIN, RBM15B, DUSP28, and TRIM31, which demonstrated the differential gene expression and prognostic value in TCGA and GEO databases. Tissue microarray analysis (TMA) revealed that TIPIN levels were altered in HCC. The upregulated TIPIN expression was associated with worse overall survival. Univariate and multivariate analyses showed that the TIPIN expression was an independent predictor of HCC. Conclusion. TIPIN might be a potential novel prognostic biomarker for HCC.