bluuebunny/crossref_metadata_2025 huggingface dataset:
Top Down and Bottom Up Synthesized Graphene Quantum Dots As Nanothermometers For I
n Vitro Imaging
Authors: Lee Bong Han , McKinney Ryan L. , Hasan Md. Tanvir , Naumov Anton V.
Published: July 2022

Non-invasive temperature sensing is necessary to analyze biological processes occurring in the human body, including cellular enzyme activity, protein expression, and ion regulation. To probe temperature-sensitive processes at the nanoscale, novel luminescence nanothermometers are developed based on graphene quantum dots (GQDs) synthesized via top-down (RGQDs) and bottom-up (N-GQDs) approaches from reduced graphene oxide and glucosamine precursors, respectively. Because of their small 3–6 nm size, non-invasive optical sensitivity to temperature change, and high biocompatibility, GQDs enable biologically safe sub-cellular resolution sensing. Both GQD types exhibit temperature-sensitive yet photostable fluorescence in the visible and near-infrared for RGQDs, utilized as a sensing mechanism in this work. Distinctive linear and reversible fluorescence quenching by up to 19.3% is observed for the visible and near-infrared GQD emission in aqueous suspension from 25 °C to 49 °C. A more pronounced trend is observed with GQD nanothermometers internalized into the cytoplasm of HeLa cells as they are tested in vitro from 25 °C to 45 °C with over 40% quenching response. Our findings suggest that the temperature-dependent fluorescence quenching of bottom-up and top-down-synthesized GQDs studied in this work can serve as non-invasive reversible/photostable deterministic mechanisms for temperature sensing in microscopic sub-cellular biological environments.

Figure 1
High Throughput Evolution of DNA-Assembled Carbon Nanotubes for Neurochemical Optical Nanosensors
Authors: Jeong Sanghwa , Landry Markita P , Navarro Nicole , Ouassil Nicholas , O’Donnell Travis Del Bonis , Beyene Abraham
Published: July 2022

Release and reuptake of neuromodulator is central to mood regulation and neuropsychiatric disorders, whereby imaging neurochemicals is of fundamental importance to study the neurochemistry signaling system. Recently, I presented a reversible near-infrared optical probe for serotonin that reports physiologically-relevant serotonin concentrations on relevant spatiotemporal scales, and is compatible with pharmacological tests. Synthetic molecular recognition for serotonin was conferred by evolving molecular recognition between single stranded DNA (ssDNA) and single-walled carbon nanotube (SWNT). To do so, we developed a high-throughput screening platform for evolution of serotonin molecular selectivity, in which systematic evolution of ligands by exponential enrichment is implemented on carbon nanotube surfaces, a process we’ve termed SELEC. Following the first SELEC result, I will represent the data analysis of DNA sequence library acquired from serotonin SELEC screening, which will shed the light for data-driven discovery of novel optical neurosensory. Our results suggest evolution of nanosensors could be generically implemented to rapidly develop other neuromodulator probes, and that these probes can image neuromodulator dynamics at spatiotemporal scales compatible with endogenous neuromodulation.
Carbon Nanotube Uptake in Photosynthetic Bacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics
Authors: Boghossian Ardemis Anoush , Antonucci Alessandra , Reggente Melania , Roullier Charlotte , Gillen Alice , Lambert Benjamin , Mouhib Mohammed
Published: July 2022

The internalization of fluorescent single-walled carbon nanotubes (SWCNTs) in photosynthetic microbes can be exploited for applications ranging from energy conversion and imaging to biomolecule delivery. The internalization of these nanoparticles are largely controlled by the permeability of the surrounding microbial cell walls, which vary in surface charge density, composition, thickness, and elasticity across different species of microbes. Since these properties play a critical role in controlling internalization, the extension of SWCNT-based technologies to prokaryotes thus requires a comparative assessment of uptake across different species.

Herein, we extend a technology previously developed for internalizing SWCNTs in prokaryotes, specifically unicellular Synechocystis sp. PCC 6803, to Nostoc punctiforme, a filamentous cyanobacterial strain known to differentiate into heterocysts under nitrogen starvation. Heterocysts are specialized compartments within the filament that are capable of nitrogen fixation. These compartments possess cell walls that are distinct from vegetative cells, conferring heterocysts with relatively reduced permeability and increased resistance to mechanical and osmotic stress. Using a combination of near-infrared (NIR) fluorescence, scanning electron microscopy (SEM), and Raman spectroscopy, we investigate the effects of different cell-wall architectures and SWCNT functionalizations on internalization. We specifically examine the effects of these distinct SWCNT-cell interactions on long-term cell integrity, activity, and viability. We further show that local variations in membrane structure can dictate the extent of SWCNT association and uptake in Nostoc cells. This dependency of nanoparticle internalization on cell type can be exploited for the development of novel cell-specific staining and imaging technologies.
Measure and Analysis of Carbon Nanotube Diffusion in 3D
Authors: Lee Antony , Caicedo Karen , Grésil Quentin , Bon Pierre , Cognet Laurent
Published: July 2022

Measuring carbon nanotube diffusion is complex in 3D liquid environments. Single molecule fluorescence microscopy commonly provides nanotube trajectories in the 2D imaging plane with nanometer precisions but assessing the third dimension is more challenging task. To this aim, We will present two strategies based on point-spread function (PSF) engineering [1] or self-interfering PSF [2]. Because nanotubes are not spherical objects, 3D angular diffusion of the nanotubes shall also be considered. We will show that using a high-frame rate imaging (kHz) of nanotube movements, the autocorrelation time of nanotube fluorescence intensity can be computed in order to measure the rotational diffusion coefficient of the nanotubes. This further allows to estimate the length of the nanotubes either from the rotational diffusion coefficients alone, or by combining translational and rotational diffusion coefficients which has the advantage to avoid the requirement of knowing the solution viscosity or the SWCNT hydrodynamic diameter [3].

References

[1] Gresil, Lee, et al. In preparation

[2] Caceido, Lee, et al. In preparation

[3] Lee & Cognet J. Appl. Phys. 128 (2020) 224301
(Invited) DNA-Controlled Carbon Nanotube Functionalization
Authors: Zheng Ming
Published: July 2022

In this talk, I will present our work on DNA-controlled functionalization of single-chirality carbon nanotubes, using the photochemical Guanine oxidation reaction first reported by the Weisman group (ACS Nano, 2019, 13, 8222). By employing resonance Raman and fluorescence spectroscopy, we are able to reveal strong sequence-dependent modification of carbon nanotubes. We have also used cryo-EM to characterize the reaction product. Our results provide new insight into the reaction mechanism.
Exploring the Covalent Doping of Single-Wall Carbon Nanotubes Induced By Photoexcited Hypochlorite
Authors: Espinoza Vanessa Briana , Zheng Yu , Htoon Han , Bachilo Sergei M. , Weisman R. Bruce
Published: July 2022

Chemical functionalization of the sidewalls of single-wall carbon nanotubes remains a topic of current interest because it can modify and enhance nanotube optical properties. These modifications can shift SWCNT structure-specific optical transitions further into the near-infrared, which may prove useful for applications in bio-imaging or quantum information processing. Here we present a study on the use of photoexcited hypochlorite ions as a reactant to efficiently produce sparse oxygen-containing defects in SWCNTs. Our studies highlight the formation of new secondary reaction products in the absence of dissolved oxygen. These results in combination with quantum computations provide insight into the structure of the new photoadducts. Additionally, we find that product spectra depend on pH of the reacting system, implying more than one reaction pathway. This finding is supported by measurements of the effects of radical quenchers on product properties. Our results provide improved understanding and control of an important class of nanotube functionalization reactions.
(Invited) Solution-Processable Carbon Nanotube Nanohybrids for Multiplexed Photoresponsive Devices
Authors: Palma Matteo
Published: July 2022

In this talk, we will present assembly strategies for the construction in aqueous solution of hybrid nanostructures comprising inorganic semiconducting nanoparticles (CdS and PbS) grown on DNA-wrapped carbon nanotubes employed as templates. The organization of these hybrids in nanoscale devices, including ones employed for multiplexed photoinduced electrical response, will be discussed.

In particular, solution-processed multiplexed photoresponsive devices were fabricated from CdS-CNT and PbS-CNT nanohybrids,[1] displaying a sensitivity to a broad range of illumination wavelengths (405,532, and 650 nm). DNA-CNT and CdS-DNA-CNT devices show a drop in the current while PbS-DNA-CNT’s current increases upon light illumination, indicating a difference in the operational mechanisms between the hybrids. Furthermore, the ON/OFF photoresponse of PbS-DNA-CNT is only 1 s as compared to 200 s for the other two nanohybrid devices.

The mechanisms of the different photoresponses were investigated by comparing the performance under an inert and air atmosphere, and gate dependence device analysis and transient absorption spectroscopy measurements were also conducted. The results revealed that photoinduced oxygen desorption is responsible for the slower photoresponse by DNA-CNT and CdS-DNA-CNT, while photoinduced charge transfer dominates the much faster response of PbS-DNA-CNT devices.[1] The strategy developed is of general applicability for the bottom-up assembly of CNT-based nanohybrid optoelectronic systems and the fabrication of solution-processable multiplexed devices.

[1] Adv. Funct. Mater. 2021, 31, 2105719

Figure 1
Arrays of Bundled Semiconducting Carbon Nanotubes for High Transconductance Field Effect Transistors
Authors: Foradori Sean , Arnold Michael , Gopalan Padma , Dwyer Jonathan H. , Suresh Anjali
Published: July 2022

Forming aligned arrays from bundled (rather than individualized) semiconducting carbon nanotubes can beneficially increase the density of nanotubes in the channel of field effect transistors (FETs). Here, we prepare aligned arrays of bundles at a density of 20 – 50 bundles μm-1 via tangential flow interfacial self-assembly and show that bundles can increase the transconductance of FETs when a strong gate is used. When encapsulated with an ion gel gate, the transconductance and saturated on-current increases as the diameter of the bundles increases. Devices tested at -0.6 V drain voltage using an ion gel gate achieve transconductance as high as 50 μS per bundle and 2000 μS μm-1 and on-state current as high as 1.7 mA μm-1. At low drain voltages the off-current also increases with bundling, but on/off ratios of ~105 are possible if the bundle size is limited to ~ 5 nm. Radio frequency devices with very strong gates may benefit from the increased device performance by using moderately bundled semiconducting carbon nanotube arrays.

Figure Caption: Panel (a) has transfer curves from 4 devices each with ~42 CNTs-bundles μm-1 but with different median bundle heights. Devices with larger bundles have higher transconductance and saturated on-currents. Panel (b) shows the transconductance per bundle vs the median bundle height and shows the positive correlation between bundle size (height) and transconductance per bundle. Panel (c) is a rendering of a large bundle formed from polymer wrapped CNTs.

Figure 1
Carbon Nanotubes from Synthesis to Picomolar Detection Electrochemical Sensors
Authors: Alvarez Noe , Gupta Pankaj , Rahm Connor , Gupta Vandna , Ruhunage Chethani
Published: July 2022

Electrochemistry at open ends and sidewalls of carbon nanotubes (CNTs) has been under debate, with opposing viewpoints as to which sites are more electrochemically active. A particular challenge in this field has been the ability to conduct electrochemical studies selectively at the open-ends of CNTs, without measuring contributions from the sidewalls. This talk will discuss the synthesis and assembly of CNTs into electrochemical sensor where open-ended CNTs were employed for electrochemical measurements. The assembly employs drawable CNTs that minimize sample handling and contamination, in the attempt to preserve the pristine nature of CNTs. Highly densified multiwalled carbon nanotube (HD-CNT) fibers were embedded within a polymer matrix protecting the sidewalls and limiting the reactions to the tips of the CNTs. Cyclic voltammetry was employed to examine the electrochemical properties of open-ended CNTs using a conventional bulk electrochemical cell and scanning electrochemical cell microscopy (SECCM). This assemblies have shown to detect extremely low concentrations of Pb2+ in water, neurotransmitters, NADH, furosemide (diuretic drug), and the electrodes were tested with multiple electrochemical techniques.
Voluntary participation in flipped classroom application sessions has a negligible effect on assessment outcomes in an accelerated pass-fail course
Authors: Steinel Natalie C. , Corliss Stephanie , Lee Michael W.
Published: July 2022

This study explores the use of flipped classroom teaching in a voluntary and accelerated medical school course. We found that historically high-performing students attend class, whereas historically low-performing students do not attend class as readily. Formative assessment performance appears to be more important than participation in determining the final grade. Correlation of high performance (>90%) with participation may differentiate students who excel in our curriculum from those who simply pass with superficial knowledge.
Research on Regional Energy Visualization Based on Knowledge Graph
Authors: Li Zhongbo
Published: July 2022

The equipment information in the regional energy system is difficult to retrieve and the amount of data is too large to realize intelligent application. In order to solve the problem, this paper proposes a regional energy visualization research method based on knowledge graph. Firstly, the collected regional energy information is classified. Secondly, the framework is defined for the unstructured data, which is difficult to be recognized by computer, and the hidden Markov model is used for word segmentation and labeling. Finally, the regional energy equipment information data are processed to construct the knowledge graph, the regional energy map structure information is represented by the ternary group graph structure, and the regional energy retrieval system is designed. The system can efficiently process and retrieve a large amount of natural language information in regional energy and realize the intelligence of regional energy equipment. It provides a strong guarantee for the efficient and stable operation of regional energy-related equipment and has a wide range of practical application and research value.
Hyperspectral Image Processing Technology and Its Application in Visual Communication Design
Authors: Wu Dong , Fang Zhengwei
Published: July 2022

In order to improve the effectiveness of visual communication design, this article combines hyperspectral image processing technology to construct a visual communication design system and improves the effect of visual communication design with intelligent methods. Eagerly, this article proposes a general framework for constructing a suitable shock capture scheme in combination with the entropy control method for suppressing shock instability. In addition, considering the guarantee of qualitative and entropy stability, this article adopts a variety of numerical experiments to evaluate the performance of the full-speed domain scheme. The simulation study shows that the visual communication system based on hyperspectral image processing technology proposed in this article has a very good visual communication effect and can effectively improve the visual communication design effect.
Dynamic Response of the Steel Plate Under the Impact of High-Speed Coal Projectile
Authors: Zhu Chuanjie , Yu Qi , Lu Ximiao , Lin Baiquan , Ma Cong
Published: July 2022

The coal-rock projectiles induced by gas explosions in coal mines have a strong destructive effect on mine facilities and equipment, which are mostly made of steel. The LS-DYNA is used to simulate the process of coal projectile striking the steel plate. The results show that during the failure process, the morphological change of the projectile consists of three stages including plastic deformation, partial crushing, and complete crushing, and the steel plate pits at the impact point. The duration of the stress peak value of the steel plate increases as the impact velocity increases. The farther away from the center point, the smaller the stress peak value is, and the shorter the duration is. When the impact velocity is 100 m/s and 300 m/s, the axial velocity curve at the impact point of the projectile is pulsating. When the impact velocity reaches 500 m/s, the axial velocity of the projectile rapidly increases and then tends to be stable. As the impact velocity increases, the energy absorbed by the steel plate increases, and the rate of increase also increases. However, the proportion between the absorbed energy of the steel plate and the initial kinetic energy of the projectile decreases. The projectile conversion energy of the 1 mm steel plate is slightly larger than that of 1.5 mm and 2 mm steel plates, and the energy absorbed by the steel plate decreases with the increase in thickness of the steel plate. When the thickness of the steel plate is more than 2 mm, the thickness of the steel plate has little effect. The results of the study have a direct sense for the antiknock design of coal mine facilities and equipment.
Intelligent Big Information Retrieval of Smart Library Based on Graph Neural Network (GNN) Algorithm
Authors: Pang Lu
Published: July 2022

In order to provide users with more humanized and intelligent big data knowledge services, a research method of intelligent big information retrieval of Smart Library Based on graph neural network (GNN) algorithm is proposed. Through the key technical problems and solutions of information recommendation represented by graph neural network (GNN) algorithm, this method explores how the library can realize the management and value mining of big data knowledge services. The research shows that the intelligent information retrieval of Smart Library Based on graph neural network (GNN) algorithm is 80% higher than the previous general methods. Graph neural network is a more advantageous algorithm for node classification, link prediction, node clustering, or network visualization, which is of great help to improve the efficiency of information retrieval.
Application of Medical-Nursing-Assistance Integration Model Based on Theoretical Basis of Behavioral Psychology in Management of Children’s ICU
Authors: Jiang Yingying
Published: July 2022

The children’s intensive care unit is a closed management area with limited visiting time and no accompanying persons. It fails to systematically reflect and summarize the opinions and needs of the families of the children. The more critically ill the family members are, the higher the requirements for medical care. Good relationship between doctors, nurses, assistant, and patients can promote the rehabilitation of children’s diseases and achieve the advanced medical model level of “seamless management and no loopholes.” In order to aid the complete intensive care process, it is vital to understand children’s psychological and physical development based on children’s behavioral psychology when the medical-nursing-assistance (MNA) integration model is used in the children’s intensive care unit. Therefore, this paper has completed the following tasks: (1) the development status of the domestic and foreign MNA integration model in the quality management of children’s intensive care units is introduced, and the MNA integration model based on the theoretical basis of behavioral psychology is proposed for the following article in children’s intensive care. The effect evaluation system of room management provides a theoretical basis. (2) The principle of BP neural network is introduced, and the effect evaluation model of the integrated mode of MNA based on BPNN in the management of children’s intensive care unit is constructed. (3) The relevant data collected are used to form an available data set for the model accuracy test. The experimental results show that, after the research in this paper, the BPNN model proposed in this paper is introduced into the MNA integration model to evaluate the effect of the management of children’s intensive care units which is practical and effective.
The Control Index for the Construction of Sponge City in the Residential Area: A Case Study of Nanjing Jiangbei New District
Authors: Liu Jianshi , Yang Jing , Zhang Hao
Published: July 2022

The construction of sponge cities is a crucial measure for the development of an ecologically sensitive urban civilization in China, but a systematic and comprehensive construction control target has not yet been established. Considering the Nanjing Jiangbei New District as an example, this article explores how to decompose the control objectives of stormwater management in urban regions into four levels, namely, urban areas, control planning units, implementation plots, and individual technical measures, which will form the basis for the design of sponge cities. The findings of this study are expected to serve as a reference for other regions of China in the design of rainwater management systems, which form the structural backbone of the sponge cities.
Assessing the Readability of Anesthesia‐Related Patient Education Materials from Major Anesthesiology Organizations
Authors: Pashkova Anna , Bangalore Raksha , Tan Cynthia , Svider Peter F. , Korban Anna , Yam Yee-Kuen , Chaudhry Faraz , Eloy Jean Anderson , Eloy Jean Daniel
Published: July 2022

Introduction. The National Institutes of Health (NIH), American Medical Association (AMA), and the US Department of Health and Human Services (USDHHS) recommend that patient education materials (PEMs) be written between the 4th to 6th grade reading level to ensure readability by the average American. In this study, we examine the reading levels of online patient education materials from major anesthesiology organizations. Methods. Readability analysis of PEMs found on the websites of anesthesiology organizations was performed using the Flesch Reading Ease score, Flesch‐Kincaid Grade Level, Simple Measure of Gobbledygook, Gunning Frequency of Gobbledygook, New Dale‐Chall test, Coleman‐Liau Index, New Fog Count, Raygor Readability Estimate, the FORCAST test, and the Fry Score. Results. Most patient educational materials from the websites of the anesthesiology organizations evaluated were written at or above the 10th grade reading level. Conclusions. Online patient education materials from the major anesthesiology societies are written at levels higher than an average American adult reading skill level and higher than recommended by National Institute of Health, American Medical Association, and US Department of Health and Human Services. Online resources should be revised to improve readability. Simplifying text, using shorter sentences and terms are strategies online resources can implement to improve readability. Future studies should incorporate comprehensibility, user‐friendliness, and linguistic ease to further understand the implications on overall healthcare.
An Automatic Architecture Designing Approach of Convolutional Neural Networks for Road Surface Conditions Image Recognition: Tradeoff between Accuracy and Efficiency
Authors: Wu Mingjian , Kwon Tae J.
Published: July 2022

Convolutional neural network (CNN) is a promising image recognition technique for winter road surface condition (RSC), a measure that is crucial for winter maintenance operations. In the past, researchers have designed RSC CNN models that displayed acceptable results but did so focusing solely on obtaining high classification accuracy without any consideration for efficiency. Furthermore, when it comes to model development itself, architecture design requires expertise in CNN as well as rich knowledge in the investigated problem itself. To rectify these issues, this paper proposes an innovative approach to automatically design RSC CNN architecture without compromising classification accuracy. The proposed approach uses a weighted sum method, which provides the freedom of choosing relative importance level between accuracy and efficiency. Once the relative importance has been set, one of the most successful and widely adopted heuristics, namely, simulated annealing (SA), is employed to generate (sub)optimal solutions. Results show that both accuracy and efficiency of the automatically generated CNNs are better or at least comparable to the two selected state-of-the-art CNN models, ResNet50 and MobileNet, achieving as high as 93.44% classification accuracy. Ultimately, the outcome of this study fills the gap in existing CNN design methods that do not consider the tradeoff between accuracy and efficiency while providing insight into the effect varying architectures have on CNN model performance.
Establishment of Narrow Root Fracture Models Using Modified Temperature Cycling Method and Diagnosis Using Different Cone-Beam CT Units
Authors: Liang Jiahao , Hu Ziyang , Cao Dantong , Cao Ya , Xie Xin , Gao Antian , Wang Zhiyong , Lin Zitong
Published: July 2022

Aim. Using a modified thermal cycling method to establish narrow root fracture models and evaluate the diagnosis efficiency of them using four different cone-beam CT (CBCT) units. Methodology. Fifty-six intact teeth were selected, and the crowns of the teeth were embedded using general purpose acrylic resin. 50 root fracture models were established by soaking these teeth in liquid nitrogen and hot water cyclically; 6 teeth were used as the negative control. All the 56 teeth were scanned with the smallest voxel size of four different CBCT units (NewTom VGi, Planmeca Promax 3D Max, Kavo 3D eXam, and Soredex Scanora3D). 10 teeth were randomly selected, and the roots were sliced using slow-speed saw to obtain horizontal root sections. Scanning electron microscope (SEM) was used to measure the width of the fracture lines (FLs). The CBCT images were evaluated for the presence or absence of fracture lines. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the diagnosis of FLs using the four CBCT units. Results. Fifty narrow root fracture models were successfully established, and 25 root sections with 45 FLs were acquired. The width of FLs was from 3.43 μm to 143 μm; 32.2% of the points had width under 25 μm, while only 9.6% of them had width over 75 μm. The accuracy was 0.41, 0.54, 0.41, and 0.30 for NewTom VGi, Planmeca Promax 3D Max, Kavo 3D eXam, and Soredex Scanora3D, respectively. Conclusions. The modified temperature cycling method is a simple and effective method to establish narrow root fracture models, and the diagnosis efficiency for these narrow fracture lines was quite poor using all the four different CBCT units.
Study on the Deformation and Stability of Proppant Column in High-Conductivity Channel Fracturing
Authors: Wang Ming , Zhang Feng , Wang Hanxiang
Published: July 2022

The nonlinear constitutive model of the proppant column was established through laboratory experiments on the stability of the proppant column. Based on reservoir geomechanics and the finite element method, a fracture-proppant column interaction model was established for high-conductivity channel fracturing. The effects of in situ stress, reservoir rock elastic parameters, and spatial distribution characteristics of the proppant column on the closure deformation of the high-conductivity fracture channel and the stability of the proppant column were studied. The higher the in situ stress, the higher the contact stress on the rock plate; the lower the height and the larger the diameter of the proppant column, the more prone to deformation and breakage, while the more the effective support decreases with the increase of the in situ stress. Under the condition of constant in situ stress, with the increase of the reservoir elastic modulus, the relative axial displacement of the two slabs decreases gradually, the effective propping ratio of fractures increases, and the reservoir elastic modulus has little effect on the stability of the proppant column. The effective propping ratio decreases with the increase of the proppant column diameter, increases with the increase of the proppant column height, and increases with the increase of the ratio of the reservoir elastic modulus to in situ stress. When the proppant column diameter (proppant column spacing) is less than 3 m, the effective propped fracture ratio increases significantly. Through the above research, the optimal proppant cluster diameter was finally optimized.
Certain Analytic Formulas Linked to Locally Fractional Differential and Integral Operators
Authors: Aldawish Ibtisam , Ibrahim Rabha W.
Published: July 2022

The present investigation is aimed at defining different classes of analytic functions and conformable differential operators in view of the concept of locally fractional differential and integral operators. We present a novel generalized class of analytic functions, which we call it locally fractional analytic functions in the open unit disk. For the suggested class, we look at conditions to get the starlikeness and convexity properties.
Novel Evaluation of Fuzzy Fractional Biological Population Model
Authors: Alyusof Rabab , Alyusof Shams , Iqbal Naveed , Samura Sallieu Kabay
Published: July 2022

This article discusses an iterative transformation method via fuzziness that mixtures the Laplace transform with the iterative iterative method. Using Caputo derivative operator, the proposed technique demonstrates the inherent reliability of fractional fuzzy biological population equations with initial fuzzy conditions. The obtained results to the fuzzy fractional biological equations are more general and apply to a broad variety of problems. A parametric description of the solutions is obtained by translating the fuzzy fractional differential equation into an equivalent system of corresponding fractional differential equations. The proposed method is numerically tested against crisp solutions and those produced by other methods, demonstrating that it is a convenient and remarkably accurate way to solve a tool for solving a wide variety of physics and engineering problems.
The Role of SliTrk5 in Central Nervous System
Authors: Liu Yan , Zhang Linming , Mei Rong , Ai Mingda , Pang Ruijing , Xia Di , Chen Ling , Zhong Lianmei
Published: July 2022

SLIT and NTRK‐like protein‐5 (SliTrk5) is one of the six members of SliTrk protein family, which is widely expressed in the central nervous system (CNS), regulating and participating in many essential steps of central nervous system development, including axon and dendritic growth, neuron differentiation, and synaptogenesis. SliTrk5, as a neuron transmembrane protein, contains two important conservative domains consisting of leucine repeats (LRRs) located at the amino terminal in the extracellular region and tyrosine residues (Tyr) located at the carboxyl terminal in the intracellular domains. These special structures make SliTrk5 play an important role in the pathological process of the CNS. A large number of studies have shown that SliTrk5 may be involved in the pathogenesis of CNS diseases, such as obsessive‐compulsive‐disorder (OCD), attention deficit/hyperactivity disorder (ADHD), glioma, autism spectrum disorders (ASDs), and Parkinson’s disease (PD). Targeting SliTrk5 is expected to become a new target for the treatment of CNS diseases, promoting the functional recovery of CNS. The purpose of this article is to review the current research progression of the role of SliTrk5 in CNS and its potential mechanisms in CNS diseases.
Web Service Recommendation Based on Word Embedding and Node Embedding
Authors: Jiang Bo , Li Hang , Yang Junchen , Qin Yanbin , Wang Liuhai , Pan Weifeng
Published: July 2022

Thanks to the rapid development of service-oriented computing (SOC) technologies, the number of Web services, such as Web API, is increasing rapidly. However, this brings some difficulties for mashup (a kind of Web API composition) developers to choose appropriate Web Services to build their projects. Finding required Web APIs from a large number of candidates and recommending them to developers has become a vital issue in mashup development. The traditional collaborative filtering algorithm has the problems of cold start and sparse data. In order to solve the deficiency of the collaborative filtering algorithm, we propose an improved hybrid method that combines the two kinds of information to generate word embedding and node embedding, avoiding the cold start problem and data sparsity problem. Experiments on real-world data sets show that our proposed approach is better than five state-of-the-art approaches, which verifies the effectiveness of our approach.
Expressions of Gratitude Applied to Business: A Lesson for Managing Online Reviews
Authors: Farias Ana R. , Simão Cláudia , Reis Joana
Published: July 2022

Online reviews are critical for businesses to thrive, but their management is not often effective. Using data from social media platforms, with more than 600 observations of public online interactions between business owners and customers, we showed that strategic management of online reviews predicts a positive increment of online reputation. Publicly expressing gratitude (Study 1) and, specifically, directing these expressions towards beneficial online reviews (Study 2) are effective strategies supporting a general increase of the business online score. These findings identify public expressions of gratitude as a responsive, attentive gesture that signals care and consideration towards customers. Such gesture promotes the online reputation through satisfaction between business community relationships.
Analysis of Strengthening Mechanism of the Steep Slope Embankment through Centrifugal Model Test
Authors: Liu Jie , Wang Bin , Sun Yunlong , Wang Bin
Published: July 2022

Centrifuge model tests were conducted to investigate the geogrid-gravelly-soil-reinforced steep slope embankments. A control experiment was carried out on a set of unreinforced embankments. The following test results were obtained. Under a centrifugal force, the unreinforced gravelly soil steep embankments failed in sudden collapse and circular sliding. By contrast, the reinforced embankments exhibited a local deformation on the slope surface and a continuous progressive deformation on the slope due to the failure of geogrid-soil interface. The geogrid reinforcement strengthened the soil and enhanced the integrity of the embankment. Thus, the stability of the slope was improved, and the horizontal lateral displacement of the slope and the settlement at the slope crest were reduced. In the design of a geogrid, reinforcements should be sparsely arranged in the upper section of the embankment and densely arranged in the lower section. For equally spaced reinforcements, the geogrid should be strengthened in the middle and lower sections of the embankment.
Robot Target Localization and Visual Navigation Based on Neural Network
Authors: Guo Haifeng , Wang Yiyang , Yu Guijun , Li Xiang , Yu Baoqi , Li Wenyi
Published: July 2022

Deep neural network has been widely used in image analysis, speech recognition, target detection, semantic segmentation, face recognition, automatic driving, and other fields due to its excellent algorithm performance. In this research, the neural network is mainly used to simulate robot target localization and visual navigation. Firstly, the data set based on ICP algorithm is constructed, and then, the navigation and positioning of robot are simulated by using neural network. The results show that the combination of ICP algorithm and direct method can effectively solve the problem of pose loss and expand the indoor range of data collection. The method using artificial neural network is effective and has better robustness and stability than the method based on frame matching. From the perspective of vision, positioning and navigation based on a single RGB image are feasible, and the processing time is relatively short, which can meet the requirements of real time and has higher practical value in general indoor scenes.
Screening of Prognostic Markers for Hepatocellular Carcinoma Patients Based on Multichip Combined Analysis
Authors: Dong Yong , Miao Qian , Li Da
Published: July 2022

Object. Due to challenges in HCC prognosis evaluation, novel prognosis-related markers are urgently needed. This study attempted to screen reliable prognostic HCC prognostic markers by bioinformatics methods. Methods. GSE (14520, 36376, 57957, 76427) datasets were accessed from GEO database. 55 differential mRNAs (DEGs) were obtained by differential analysis based on the datasets. GO and KEGG analysis results indicated that the DEGs were enriched in xenobiotic metabolic process and other pathways. Expression profiles and clinical data of TCGA-LIHC mRNAs were from TCGA database. We established a prognostic model of HCC through univariate and multivariate Cox risk regression analyses. ROC curve analysis was used to examine the prognostic model performance. GSEA analysis was performed between the high- and low-risk score sample groups. Results. A 4-gene HCC prognostic model was constructed, in which the gene expressions correlated to HCC patients’ survival. The AUC value presented 0.734 in the ROC analysis for the prognostic model. Conclusion. The four-gene model could be introduced as an independent prognostic factors to assess HCC patients’ survival status.
Private Computing Offloading in Edge Cloud via Collaborative Online Learning
Authors: Wang Lin , Yang Lei , Zhang Mingchuan , Zhang Jianxin , Cao Zhibin , Wu Qingtao
Published: July 2022

Computing offloading based on mobile edge computing (MEC) for mobile devices (MDs) has received great attentions in recent years. Strategy selection is an extremely important part of computing offloading, so how to make an optimal decision quickly and accurately during the computing offloading is a difficult point. Furthermore, MDs are likely to leak personal privacy when interacting with edge cloud, and there is also an issue about commercial privacy leakage between different cloud service suppliers. In this paper, we propose the privacy-protected edge cloud computing offloading (EPCO) algorithm based on online learning to improve the efficiency of computing offloading while ensuring the privacy of system users. Simultaneously, EPCO also supports different MDs customize their privacy level. We prove that adding privacy protection mechanism is almost no effect on the convergence of the algorithm. The simulation results validate our conclusion using a real-world dataset.
Deformation and Fracture Characteristics of Coal Gangue Interbedded Samples under Loading and Unloading Conditions
Authors: Xue Jisheng , Zhao Tielin
Published: July 2022

The dynamic sedation of the coal mine production is alternate or superimposed in coal rocks, which causes crack unevenness due to the presence of jacquards, causing unregistered coal blocks. By observing the deformation and fracture characteristics of coal‐rock interbedding specimens with different gangue ratio under different stress paths, this paper studies the fracture instability mechanism and fracture characteristics of coal‐rock interbedding, analyzes the progressive failure process and fracture opening model of coal‐rock interbedding, and uses GDEM software to simulate the triaxial loading and unloading failure characteristics of coal‐rock samples. The results show that the ratio of gangue inclusion has a significant effect on the structural strength of coal rock interbedding; with the unloading, the coal rock interbedding of the top coal body will be damaged and destroyed to varying degrees and asynchronously, the coal rock interbedding of the top coal body will present various forms of failure characteristics according to the changes of horizontal stress gradient in the vertical direction; the progressive failure process of coal rock interbedding under loading and unloading conditions can be divided into three stages. As the jigs are raised, the crack nonstable expansion stress of the composite coal body sample is gradually increased; and the rising ratio of the jacket X is not complete. The striped conjugated shear failure characteristics are more obvious; the different combined coal gangue tester shows microcrack near the peripheral loading boundary, and as the loading axis pressure increases, the fracture distribution range and density are increased; the rock layer is present. The tensile fracture is destroyed, and the coal seam exhibits shear destruction of intensive cracks; different test pieces produce significant tensile destruction on one side of the unloaded boundary; the greater the loaded axial pressure of the specimen, the greater the damage degree of the specimen after unloading; the damage degree of two coal and one rock specimens is the largest, the damage degree of one coal and one rock specimens is in the middle, and the damage degree of one coal and two rock specimens is the smallest.
Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System
Authors: Shao Ruihua
Published: July 2022

In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.
Quantization of the Super-BMS3 Algebra
Authors: Yang Yu , Wang Xing Tao
Published: July 2022

In this article, we study quantization of super-BM

S

3

algebra

W

. We quantize

W

by the Drinfel’d twist quantization technique and obtain a class of noncommutative and noncocommutative Hopf superalgebras.
Priority-Based Resource Allocation and Energy Harvesting for WBAN Smart Health
Authors: Selvaprabhu Poongundran , Chinnadurai Sunil , Tamilarasan Ilavarasan , Venkatesan Rajeshkumar , Kumaravelu Vinoth Babu
Published: July 2022

With the emergence of new viral infections and the rapid spread of chronic diseases in recent years, the demand for integrated short-range wireless technologies is becoming a major bottleneck. Implementation of advanced medical telemonitoring and telecare systems for on-body sensors needs frequent recharging or battery replacement. This paper discusses a priority-based resource allocation scheme and smart channel assignment in a wireless body area network capable of energy harvesting. We investigate our transmission scheme in regular communication, where the access point transmits energy and command while the sensor simultaneously sends the information to the access point. A priority scheduling nonpreemptive algorithm to keep the process running for all the users to achieve the maximum reliability of access by the decision-maker or hub during critical situations of users has been proposed. During an emergency or critical situation, the process does not stop until the decision-maker or the hub takes a final decision. The objective of the proposed scheme is to get all the user processes executed with minimum average waiting time and no starvation. By allocating a higher priority to emergency and on data traffic signals such as critical and high-level signals, the proposed transmission scheme avoids inconsistent collisions. The results demonstrate that the proposed scheme significantly improves the quality of the network service in terms of data transmission for higher priority users.
State Evaluation Method of Distribution Terminal Based on Deep Reinforcement Learning
Authors: Xue Fei , Li Xutao , Wang Xiaoli , Li Hongqiang , Tian Bei
Published: July 2022

The reliable operation of the distribution terminal is a key link to realize distribution automation. It is particularly important to efficiently and accurately evaluate the operation state of the distribution terminal. In order to realize accurate state perception of distribution terminals, a state evaluation method based on deep reinforcement learning is proposed to support the reliable operation of the distribution network. First, the fault causes of terminal equipment and the collected datasets are introduced. On this basis, the multilayer network structure is used to analyze the terminal state. Q-reinforcement learning network is used to optimize the convolution neural network, solve the overfitting problem of the deep network model, and continuously extract the data features. At the same time, in order to increase the objectivity and reliability of the evaluation method, the membership function optimization is also introduced into the model to further ensure the accuracy of the state analysis method. Simulation results show that the recognition accuracy of the proposed method is 94.23%, which shows excellent evaluation performance.