bluuebunny/crossref_metadata_2025 huggingface dataset:
Generic generation of noise-driven chaos in stochastic time delay systems: Bridging the gap with high-end simulations
Authors: Chekroun Mickaël D. , Koren Ilan , Liu Honghu , Liu Huan
Published: November 2022

Nonlinear time delay systems produce inherently delay-induced periodic oscillations, which are, however, too idealistic compared to observations. We exhibit a unified stochastic framework to systematically rectify such oscillations into oscillatory patterns with enriched temporal variabilities through generic, nonlinear responses to stochastic perturbations. Two paradigms of noise-driven chaos in high dimension are identified, fundamentally different from chaos triggered by parameter-space noise. Noteworthy is a low-dimensional stretch-and-fold mechanism, leading to stochastic strange attractors exhibiting horseshoe-like structures mirroring turbulent transport of passive tracers. The other is high-dimensional , with noise acting along the critical eigendirection and transmitted to “deeper” stable modes through nonlinearity, leading to stochastic attractors exhibiting swarm-like behaviors with power-law and scale break properties. The theory is applied to cloud delay models to parameterize missing physics such as intermittent rain and Lagrangian turbulent effects. The stochastically rectified model reproduces with fidelity complex temporal variabilities of open-cell oscillations exhibited by high-end cloud simulations.
Sustainable valorization of asphaltenes via flash joule heating
Authors: Saadi M.A.S.R. , Advincula Paul A. , Thakur Md Shajedul Hoque , Khater Ali Zein , Saad Shabab , Shayesteh Zeraati Ali , Nabil Shariful Kibria , Zinke Aasha , Roy Soumyabrata , Lou Minghe , Bheemasetti Sravani N. , Bari Md Abdullah Al , Zheng Yiwen , Beckham Jacob L. , Gadhamshetty Venkataramana , Vashisth Aniruddh , Kibria Md Golam , Tour James M. , Ajayan Pulickel M. , Rahman Muhammad M.
Published: November 2022

The refining process of petroleum crude oil generates asphaltenes, which poses complicated problems during the production of cleaner fuels. Following refining, asphaltenes are typically combusted for reuse as fuel or discarded into tailing ponds and landfills, leading to economic and environmental disruption. Here, we show that low-value asphaltenes can be converted into a high-value carbon allotrope, asphaltene-derived flash graphene (AFG), via the flash joule heating (FJH) process. After successful conversion, we develop nanocomposites by dispersing AFG into a polymer effectively, which have superior mechanical, thermal, and corrosion-resistant properties compared to the bare polymer. In addition, the life cycle and technoeconomic analysis show that the FJH process leads to reduced environmental impact compared to the traditional processing of asphaltene and lower production cost compared to other FJH precursors. Thus, our work suggests an alternative pathway to the existing asphaltene processing that directs toward a higher value stream while sequestering downstream emissions from the processing.
Creating stable trapping force and switchable optical torque with tunable phase of light
Authors: Nan Fan , Li Xiao , Zhang Shuailong , Ng Jack , Yan Zijie
Published: November 2022

Light-induced rotation of microscopic objects is of general interest as the objects may serve as micromotors. Such rotation can be driven by the angular momentum of light or recoil forces arising from special light-matter interactions. However, in the absence of intensity gradient, simultaneously controlling the position and switching the rotation direction is challenging. Here, we report stable optical trapping and switchable optical rotation of nanoparticle (NP)–assembled micromotors with programmed phase of light. We imprint customized phase gradients into a circularly polarized flat-top (i.e., no intensity gradient) laser beam to trap and assemble metal NPs into reconfigurable clusters. Modulating the phase gradients allows direction-switchable and magnitude-tunable optical torque in the same cluster under fixed laser wavelength and handedness. This work provides a valuable method to achieve reversible optical torque in micro/nanomotors, and new insights for optical trapping and manipulation using the phase gradient of a spatially extended light field.
A conserved module in the formation of moss midribs and seed plant axillary meristems
Authors: Ge Yanhua , Gao Yi , Jiao Yuling , Wang Ying
Published: November 2022

Different evolutionary lineages have evolved distinct characteristic body plans and anatomical structures, but their origins are largely elusive. For example, seed plants evolve axillary meristems to enable lateral branching. In moss, the phyllid (leaf) midrib containing specialized cells is responsible for water conduction and support. Midribs function like vascular tissues in flowering plants but may have risen from a different evolutionary path. Here, we demonstrate that midrib formation in the model moss
Physcomitrium patens
is regulated by orthologs of
Arabidopsis LATERAL SUPPRESSOR
(
LAS
), a key regulator of axillary meristem initiation. Midribs are missing in loss-of-function mutants, and ectopic formation of midrib-like structures is induced in overexpression lines. Furthermore, the
PpLAS
/
AtLAS
genes have conserved functions in the promotion of cell division in both lineages, which alleviates phenotypes in both
Physcomitrium
and
Arabidopsis las
mutants. Our results show that a conserved regulatory module is reused in divergent developmental programs, water-conducting and supporting tissues in moss, and axillary meristem initiation in seed plants.
Genome-Wide Analysis of Gene Expression Noise Brought About by Transcriptional Regulation in
Pseudomonas aeruginosa
Authors: Chen Wenhui , Zhang Jinfeng , Li Feixuan , Wang Congcong , Zhang Yuchen , Xia Aiguo , Ni Lei , Jin Fan
Published: November 2022

The phenotypic diversity of
Pseudomonas aeruginosa
is frequently observed in research, suggesting that bacteria adopt strategies such as bet-hedging to survive ever-changing environments. Gene expression noise (GEN) is the major source of phenotypic diversity.
MIAR: A Context-Aware Approach for App Review Intention Mining
Authors: Lu Jinwei , Wu Yimin , Pei Jiayan , Qin Zishan , Huang Shizhao , Deng Chao
Published: November 2022

Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users. To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective. Although there are many approaches based on intention mining that can automatically predict the intention of reviews for better understanding valuable information, those approaches are limited since contextual information of the whole review text may be lost. In this paper, we propose Mining Intention from App Reviews (MIAR), a novel deep learning model to predict the intention of app reviews automatically. We adopt a Contextual Feature Extractor to capture the context semantic information and fuse it with the local feature through a fusion mechanism. The experiment results demonstrate that MIAR has made significant improvement over the baseline approaches in Precision, Recall and [Formula: see text]-score evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in other intention mining tasks, proving its generalization ability and robustness.
Multimodal Depression Detection: Using Fusion Strategies with Smart Phone Usage and Audio-visual Behavior
Authors: Thati Ravi Prasad , Dhadwal Abhishek Singh , Kumar Praveen , Sainaba P
Published: November 2022

The problem of detecting depression is multi-faceted because of variability in depressive symptoms caused by individual differences. The variations can be seen in historical information (like decreased physical activity etc.) and also in verbal/non-verbal behaviors (like lower pitch, downward eye gaze etc.). The primary goal of this research is to develop a novel classification system for diagnosing depression that considers both historical information and also verbal/non-verbal behaviors. For this purpose, we created a realworld multimodal dataset of depressed and non-depressed subjects with fourteen-day real-time smartphone usage records and audio-visual recordings. We extracted numerous features related to physiological/physical activity from smartphone usage records to capture historical information and features like pitch and eye gaze (verbal and non-verbal manifestations) from audio-visual clues. We experimented with early fusion using Decision trees classifier (along with several feature selection strategies) and Support Vector Machine (SVM) classifier with several late fusion methods. Then, we conducted a comparative study among both fusion strategies. Our findings showed that SVM classifier using late fusion strategy achieves best accuracy of 89%. In addition, a popular benchmarking multimodal dataset (DAIC-WOZ database) is used to further validate the effectiveness of our approach by fusing multi-faceted feature vectors for depression detection.
Tunable plasmonically-induced reflection in MIM waveguide structure with split ring resonator
Authors: Lin Shuang , Chen Yunfei , Liu Xuebo , Zhang Baohua , Zhang Zheng , Guo Fuqiang
Published: November 2022

A novel plasmonic waveguide structure consisting of a rectangular resonator side coupled with a split ring resonator (SRR) and a metal–insulator–metal (MIM) waveguide with metal baffle was proposed. The transmission characteristics of the present structure were simulated by a finite-difference time-domain (FDTD) method. The results show that the structure can realize three typical plasmonically-induced reflection (PIR) windows. Meantime, the PIR window can be tuned vertically and horizontally, respectively, and multiple PIR windows with different wavelength intervals can also be obtained by changing the split position. In addition, the sensitivity of the proposed structure can reach up to 1,075[Formula: see text]nm/RIU. Furthermore, a rectangular resonator was added to the other side of the plasmonic waveguide structure for realizing the dual-channel PIR window. Another SRR was added to form a symmetrical structure, which further improved the sensitivity and transmittance. Undoubtedly, the proposed structure will have potential applications in sensor and optical switches.
On the non-commutative multifractional Brownian motion
Authors: Dozzi Marco , Schott René
Published: November 2022

We define a non-commutative analogue of a real gaussian Volterra-type multifractional Brownian motion (NC-mfBm for short) and show that its trajectories behave locally like non-commutative fractional Brownian motion. We determine the pointwise Hölder exponent as well as a random matrix approximation of this process.
Vibration Analysis of Composite Laminated and Sandwich Conical Shell Structures: Numerical and Experimental Investigation
Authors: Chadha Akhil , Sudhagar P. Edwin , Gunasegeran Muthukumaran , Veerappa Vimalanand Suthenthira
Published: November 2022

The use of composite conical shell structures across the globe in various applications concentrates on nose cones and incredibly complex vehicle components in the aerospace and automotive industries. This work investigates the free vibration analysis of laminated conical shell (LCS) and sandwich conical shell (SCS) structures with various foam cores. The sandwich construction is fabricated using the hand layup technique. The vibration characteristics of the fabricated LCS and SCS specimens are validated with the Finite element software (ANSYS). Very minimal variation was observed between the experimental and numerical simulations. The various parametric analyses were also studied for the LCS and SCS with length, ply orientation, and boundary conditions. Further, for SCS, vibration behaviors across multiple core materials and core thickness were also investigated.
Spline representation and redundancies of one-dimensional ReLU neural network models
Authors: Plonka Gerlind , Riebe Yannick , Kolomoitsev Yurii
Published: November 2022

We analyze the structure of a one-dimensional deep ReLU neural network (ReLU DNN) in comparison to the model of continuous piecewise linear (CPL) spline functions with arbitrary knots. In particular, we give a recursive algorithm to transfer the parameter set determining the ReLU DNN into the parameter set of a CPL spline function. Using this representation, we show that after removing the well-known parameter redundancies of the ReLU DNN, which are caused by the positive scaling property, all remaining parameters are independent. Moreover, we show that the ReLU DNN with one, two or three hidden layers can represent CPL spline functions with K arbitrarily prescribed knots (breakpoints), where K is the number of real parameters determining the normalized ReLU DNN (up to the output layer parameters). Our findings are useful to fix a priori conditions on the ReLU DNN to achieve an output with prescribed breakpoints and function values.
Gabor frame characterizations of generalized modulation spaces
Authors: Debrouwere Andreas , Prangoski Bojan
Published: November 2022

We obtain Gabor frame characterizations of modulation spaces defined via a broad class of translation-modulation invariant Banach spaces of distributions. We show that these spaces admit an atomic decomposition through Gabor expansions and that they are characterized by summability properties of their Gabor coefficients. Furthermore, we construct a large space of admissible windows. This generalizes and unifies several fundamental results for the classical modulation spaces [Formula: see text] and the amalgam spaces [Formula: see text]. Due to the absence of solidity assumptions on the Banach spaces defining these generalized modulation spaces, the methods used for the spaces [Formula: see text] (or, more generally, in coorbit space theory) fail in our setting and we develop here a new approach based on the twisted convolution.
TFA-CLSTMNN: Novel convolutional network for sound-based diagnosis of COVID-19
Authors: He Yuhao , Zheng Xianwei , Miao Qing
Published: November 2022

The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets.
Entropy of temporal entanglement
Authors: Castellani Leonardo
Published: November 2022

A recently proposed history formalism is used to define temporal entanglement in quantum systems, and compute its entropy. The procedure is based on the time-reduction of the history density operator, and allows a symmetrical treatment of space and time correlations. Temporal entanglement entropy is explicitly calculated in two simple quantum computation circuits.
Axial functionalization of subphthalocyanines and subphthalocyanine fused dimers for photodynamic therapy
Authors: Gallego Lucía , Martínez-Díaz M. Victoria
Published: November 2022

Subphthalocyanines (SubPcs) are peculiar porphyrinoid compounds, exhibiting an intense absorption in the visible region and a particular cone-shaped structure that gives rise to their characteristic reduced aggregation tendency, which makes them suitable photosensitizers for photodynamic therapy (PDT) applications. On the other hand, subphthalocyanine fused dimers (SubPc[Formula: see text] are best known for their [Formula: see text]-extended surface and strong absorption in the near IR region, while keeping a low aggregation tendency. However, they remain unexplored photosensitizers in this field. Herein, the synthesis and study of properties of several axially aryloxy substituted SubPcs and SubPc fused dimers are reported. The axial aryloxy groups additionally bear polar substituents that help increase the solubility of this family of intrinsically hydrophobic compounds in polar solvents. All synthesized SubPcs and SubPc2 showed a good ability to generate singlet oxygen (1O[Formula: see text] in experiments carried out in THF. Although [Formula: see text] values obtained with SubPc fused dimers were lower than those of the corresponding SubPcs bearing the same substituent, the results are encouraging considering their absorption maximum is centered in the therapeutic window.
Multiplicity for a strongly singular quasilinear problem via bifurcation theory
Authors: Giacomoni Jacques , dos Santos Lais Moreira , Santos Carlos Alberto
Published: November 2022

A [Formula: see text]-Laplacian elliptic problem in the presence of both strongly singular and [Formula: see text]-superlinear nonlinearities is considered. We employ bifurcation theory, approximation techniques and sub-supersolution method to establish the existence of an unbounded branch of positive solutions, which is bounded in positive [Formula: see text]-direction and bifurcates from infinity at [Formula: see text]. As consequence of the bifurcation result, we determine intervals of existence, nonexistence and in particular cases, global multiplicity.
Non-symmetry and symmetry of syzygies of a system of Cauchy functional equations with a homomorphism
Authors: Zhang Dongwen , Rassias John Michael , Liu Qi , Li Yongjin
Published: November 2022

Two entirely different Cauchy functional equations have been investigated in the literature with a couple of unknown operators f and g mapping a unitary ring X into a domain of integrity Y satisfying the elimination law and keeping the operations of addition about operator f and multiplication about operator g, respectively. A total of four studying syzygies achieved by multiplying these two fundamental Cauchy functional equations side by side are investigated. The novelty of the literature is to find out sufficient conditions upon [Formula: see text] deriving the model to be produced a strongly alienation phenomenon by using some new computation techniques.
Groundwater control for construction projects in chalk
Authors: Preene Martin , Roberts Toby
Published: November 2022

Construction excavations and tunnels in chalk can encounter groundwater challenges, including high water flow rates, instability of excavations in weathered chalk and basal instability in overlying aquitards caused by high groundwater pressures in deeper chalk aquifers. In hydrogeological settings where the chalk has been exposed to periglacial weathering during the Quaternary Period the upper zones may be degraded to structureless chalk which can potentially be of very low hydraulic conductivity (putty chalk) or very high hydraulic conductivity (chalk bearings). In deeper, structured chalk groundwater flow tends to be concentrated along fissures associated with pre-existing geological structures such as bedding planes, flint beds or faults. A range of groundwater control strategies can be deployed, including open pumping, pre-drainage pumping, shallow and deep cut-off walls, ground treatment and, for tunnels and shafts, application of fluid counter pressures to exclude groundwater. The strategy appropriate to a given site must be selected based on a thorough understanding of the hydrogeological setting and chalk weathering profile. This requires a ground investigation of appropriate scope, using suitable techniques to characterize the chalk. Borehole geophysics can play a key role in identifying discrete zones of inflow.
Resilience of Hybrid Casper Under Varying Values of Parameters
Authors: Galletta Letterio , Laneve Cosimo , Mercanti Ivan , Veschetti Adele
Published: November 2022

Hybrid Casper is the new Ethereum blockchain protocol that uses both Proof of Work and Proof of Stake to reach a consensus between nodes. Here, we analyze the protocol using
PRISM+
, an extension of the probabilistic model checker
PRISM
with primitives for expressing blockchain data types. First, we extend
PRISM+
to include data types and operations for modeling and analyzing Proof of Stake based consensus protocols. Then, we model Hybrid Casper in
PRISM+
as a parallel composition of stochastic processes, thus precisely describing the behavior of the protocol and highlighting its corner cases.
PRISM+
is therefore used to rapidly and automatically analyze the resilience of Hybrid Casper when tuning, up or down, several basic parameters of the protocol, such as the rates of creating blocks, and the strategies for determining penalties. Finally, we study the robustness of Hybrid Casper to two well-known attacks: the Eclipse attack and the majority attack.
Path Planning For Unmanned Surface Vehicles Based On Modified Artificial Fish Swarm Algorithm With Local Optimizer
Authors: Wang Fang , Zhao Liang , Bai Yong
Published: November 2022

The appeal for safe navigation of autonomous surface vessels (ASVs) has deemed the path planning problem as an attractive research interest. However, most of the previous works to solve the path planning problem focus on finding the shortest and collision-free path, but the solutions are scarcely satisfied by the safety requirements and constraints related to the USVs’ mechanical systems. To address this challenge, we present a novel path planning method based on a modified artificial fish swarm algorithm in combination with a path optimizer. The modifications are made from two perspectives: (1) Four customized operators and an adaptive factor are applied to improve the convergence performance of the algorithm. (2) A local path optimizer is proposed to enhance its feasibility of cooperating with the USV control system. Path safety, path smoothness, and nonholonomic constraints are considered. The path planning benchmark experiments have demonstrated its superior performance in terms of efficiency and path quality compared to other state-of-the-art algorithms. Moreover, the proposed method is also integrated into the USV’s control system in a practical environment with satisfactory feasibility. The simulation results provide strong evidence that the proposed method can be regarded as a practical approach for USV path planning problems.
A Prognostic Risk Model of a Novel Oxidative Stress-Related Signature Predicts Clinical Prognosis and Demonstrates Immune Relevancy in Lung Adenocarcinoma
Authors: Huang Xing , Lu Zhichao , He Min , Feng Yipeng , Yu Shaorong , Shen Bo , Lu Jianwei , Wu Pingping , Pan Banzhou , Ding Hanlin , Chen Chen , Sun Yidan
Published: November 2022

Lung adenocarcinoma (LUAD) is among the most prevalent malignant lung cancers with a poor prognosis due to high invasiveness and lethality despite multiple treatments. Since the lung is an important organ associated with oxidative stress, and it has been confirmed that oxidative stress represents a potential cancer-specific depletion, it is of important significance to investigate and evaluate the clinical value of oxidative stress mechanisms regulating tumor cell apoptosis. Furthermore, there are few studies on the impact of the microenvironment on reaction to immune-checkpoint inhibitors (ICIs) in patients with LUAD. Based on the TCGA-LUAD dataset, which is stratified into a training set as well as a validation set in a ratio of 2 : 1, this investigation constructs and validates a prognostic predictive power of a gene signature model of oxidative stress-related prognostic signatures. To ascertain the differences between the high-risk score group and the low-risk score group in tumor-infiltrating lymphocytes and patients’ response to ICI therapy. This oxidative stress-related prognostic gene signature is composed of MAP3K19 and NTSR1 and is an independent prognosis-related factor in the LUAD group. The outcome of patients having a low risk score is better, and the difference was statistically significant, and individuals with a low risk score had a larger number of infiltrating immune cell distribution in the tumor microenvironment, which was closely related to clinical outcome. Our study suggests that the synergistic effect of oxidative stress-related prognostic gene markers-MAP3K19 and NTSR1 has clinical significance in the prognosis identification and immunotherapy of LUAD patients. Thus, the results may help to better intersect the oxidative stress-related mechanisms in clinical value in LUAD but requires prospective validation.
A Large‐Scale Group Decision‐Making Consensus Model considering the Experts’ Adjustment Willingness Based on the Interactive Weights’ Determination
Authors: Bai Shizhen , He Hao , Luo Dan , Ge Mengke , Yang Ruobing , Bi Xinrui
Published: November 2022

This study proposes a large‐scale group decision‐making (LSGDM) consensus model considering the experts’ adjustment willingness based on the interactive weights’ determination, which can be applied to an LSGDM problem through a case of earthquake shelters. The main contributions of our research are of three aspects as follows. First, the determination method of the interactive weight, which obtains the DMs’ attitude towards the decision‐making results, is presented. The subgroups’ weights are calculated, and the unit adjustment cost for each DM is defined. Second, we introduce an objective consensus threshold by the mean and variance of the consensus level for each subgroup. Subsequently, an identification rule is performed to determine the DM to be adjusted with the large difference and the low adjustment cost. And we developed a novel consensus adjustment method that takes the DMs’ adjustment willingness into account to retain as much original information as possible. Thirdly, in order to reduce the subjectivity of the preset consensus threshold and the maximum number of iterations, an objective consensus termination condition that combines the current group consensus level and the consensus adjustment rate is put forward. Finally, the proposed model has demonstrated its effectiveness and superiority based on the comparative and sensitive analysis through a practical example.
Effects of Camrelizumab Combined with First-Line Chemotherapy on Serum SCC, VEGF Levels, and Adverse Reactions in Patients with Advanced Squamous Cell Carcinoma of the Lung
Authors: Wang Zizong , Xuan Yunpeng , Shao Yanmei , Zou Qiang , Cui Hong , Jin Xiangfeng
Published: November 2022

Objective. To study the effects of camrelizumab accompanied by first-line chemotherapy on serum SCC, VEGF levels, and adverse reactions in people undergoing advanced lung squamous cell carcinoma. Methods. Data sources of the study subjects were 60 people suffering from advanced squamous cell carcinoma of the lung hospitalized from January 2018 to October 2019. They were assigned to two groups, including the control group and the observation group in a random manner, and each consisted of 30 patients. Those in the observation group received camrelizumab (SHR-1210), and gemcitabine plus cisplatin (GP) chemotherapy were treated in the control group. Finally, according to the results, we compare the data of patients in both groups so as to find out the similarities and differences. Results. Among them, the effective efficiency of clinical treatment in the control group reached 36.67%, and that in the observation group reached 56.67%. Intuitively, it can be concluded that the control group showed lower results than the observed group. The observed group turned out to have higher periodic survival and progression free survival (PFS) of patients than the other group. During and after the cycle treatment, the data of SCC and VEGF were reduced to some extent, but the control group appeared to have a more evident reduction rate than the other group. Conclusion. SHR-1210 combined with chemotherapy has a considerable effect in practical application and has excellent clinical performance.
Experimental Study on the Variation of Soil Dielectric Permittivity under the Influence of Soil Compaction and Water Content
Authors: Hu Jun , Wang Xinbin , Zhang Fujun , Zhao Yuanke
Published: November 2022

The dielectric permittivity of common soils is mainly controlled by water content and porosity, while the latter is closely related to the characteristics of compaction. By studying the changes in dielectric permittivity of soil samples with different soil water content and compaction levels, the influence of the controlling factors on the relationship model between soil water content and dielectric permittivity can be evaluated. In this paper, network analyzer was used to measure the dielectric permittivity of 7 groups of soil samples with gravimetric water content ranging from 8.09% to 14.52% and dry density ranging from 1.61 g/cm3 to 1.96 g/cm3. The results show that the dielectric permittivity increases with the increase of water content and dry density, and the effect of water content on permittivity is more significant for soils with higher dry density. Furthermore, when the water content is less than or equal to the optimal water content, Topp formula and the complex refractive index model (CRIM) can better predict the soil dry density. When the water content approaches the saturated state of soil, there is a deviation between the predicted value and the actual value. At last, the modified Topp formula and the complex refractive index model (CRIM) can accurately predict soil compactness. This provides an important basis for rapid detection of water content and compactness of highway subgrade soil by ground penetrating radar.
A Theoretical Model of the Flow Properties of Postprocessed Direct Metal Laser Sintering Ti6Al4V (ELI)
Authors: Muiruri A. M. , Maringa M. , du Preez W.
Published: November 2022

Heat treatment of direct metal laser sintering (DMLS) Ti6Al4V (ELI) generates different mechanical properties of the alloy depending on the heat treatment cycle adopted. This is due to the different aspects of the microstructure, such as phase fraction, grain size, texture, and dislocation density, which vary with heat treatment. Other external factors, such as the prevailing level of strain, strain rate, and temperature, also affect the mechanical properties of the material. This paper presents the development of a theoretical model that couples the effects of strain rate, temperature, strain, grain size, and initial dislocation density to describe the flow properties of DMLS Ti6Al4V (ELI). According to the model, higher initial dislocation density results in higher yield stress, low strain hardening, and earlier saturation of flow stress. The model shows that the parabolic shape of the stress-strain curve of the alloy is dictated by the initial dislocation density, which is generally a factor of grain size.
Bushen Wenyang Huayu Decoction Targets TLR4/NF-κB Mediated Autophagy to Treat Endometriosis Effectively
Authors: Li Ying , Meng Xin , Fu Xinping , An Mingli , Liu Huimin , Ma Yiming , Li Qingxue , Hao Guorong , Ma Yucong , Zhang Yu , Yang Jian , Chen Jingwei
Published: November 2022

Endometriosis has been found to be closely related to autophagy. This study aimed to elucidate the possible mechanism of Bushen Wenyang Huayu Decoction (BWHD) in treating endometriosis (EMs) by targeting TLR4/NF-κB-mediated autophagy. Autologous grafting was used to generate the EMs model in rats. Once the model was developed, BWHD high-dose and low-dose groups received intragastric administration of BWHD, and the gestrinone group served as a positive control. Immunofluorescence labeling and Western blotting were used for the protein expression of toll-like receptor 4 (TLR4), nuclear transcription factor-κB (NF-κB), Beclin-1, and selective autophagy connector protein P62 (P62). Quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyze mRNA levels of TLR4, NF-κB, Beclin-1, and P62. We found that BWHD significantly reduced the size of ectopic lesions in rats with EMs, regulated reproductive hormone levels, and alleviated the cell autophagy level. It suggested that BWHD could be an effective treatment of EMs by targeting TLR4/NF-κB signaling pathway.
Circular RNA circ_0004488 Increases Cervical Cancer Paclitaxel Resistance via the miR-136/MEX3C Signaling Pathway
Authors: Yi Hanjie , Han Yongqing , Li Qin , Wang Xia , Xiong Le , Li Shanfeng
Published: November 2022

Circular RNAs have been proven to play a pivotal role in cervical cancer development, progression, and treatment resistance. However, it is unclear how these RNAs influence chemoresistance in cervical cancer, particularly cancer stem cell (CSC)-like properties. In this study, we found that circRNA circ_0004488 was highly expressed in CSC-enriched subsets of cervical cancer cell lines. The expression of circ_0004488 was upregulated in cervical cancer cells that were resistant to paclitaxel. When circ_0004488 expression was high, the prognosis was poor. Specifically, we discovered that knocking down circ_0004488 greatly decreased the development of cervical cancer cells in vivo by decreasing cell proliferation, invasion, and sphere formation. By blocking cir_0004488, cervical cancer cells become more sensitive to paclitaxel. In cervical cancer cells, circ_0004488 acted as a microRNA-136 (miR-136) sponge, increasing the expression of MEX3C (a direct target gene of miR-136) using dual-luciferase reporter assays. Moreover, MEX3C downregulation significantly reduced cell proliferation, invasion, sphere formation, and paclitaxel resistance. In conclusion, circ_0004488 was shown to induce CSC-like features and paclitaxel resistance through the miR-136/MEX3C axis. Therefore, circ_0004488 might be a good therapeutic target for treating cervical cancer.
Prognosis Signature of Cuprotosis-Related lncRNAs Associated with Kidney Renal Clear Cell Carcinoma
Authors: Gao Yiren , Jia Zhen
Published: November 2022

Cuprotosis is a novel cell death mechanism that can be explored to treat various tumors. A few studies on the role of cuprotosis-related long noncoding RNA (lncRNA) in the development and prognosis of kidney renal clear cell carcinoma (KIRC) have been reported. We aimed to study the relationship between the prognosis of patients suffering from KIRC and lncRNAs associated with cuprotosis. The Cancer Genome Atlas (TCGA) database was analyzed, and the transcriptome data and clinical information on the patients with KIRC were obtained. The cuprotosis-related lncRNAs were identified by using Pearson correlation analysis, and the significant changes in the lncRNAs associated with KIRC were studied by conducting the T-test. The cuprotosis-related lncRNAs with KIRC prognostic values were identified by using the univariate Cox analysis, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) methods. A prognostic marker composed of three cuprotosis-related lncRNAs was identified following the multivariate regression analysis method. Patients with KIRC were divided into two groups based on the expression characteristics of three cuprotosis-related lncRNAs by using the K nearest neighbor (KNN) cluster analysis method. Significant differences in survival were observed between the two groups. In addition, the results obtained following the independent prognostic analysis of the risk score (RS) and clinical correlation revealed that the three cuprotosis-related lncRNA prognostic markers could accurately predict the prognosis of patients with KIRC. The results reported herein provide new insights into the pathogenesis of KIRC and the contribution of lncRNAs associated with cuprotosis. The results also helped identify a prognostic indicator that could potentially provide information for KIRC treatment.
Optimization of Logging Interpretation Parameters Based on Committee Machine
Authors: Na Ziyi , Dang Lixia , Chang Mingyang , Deng Rui
Published: November 2022

Aiming at the problems of low precision and low applicability of the selection method of macroscopic capture cross-section parameters of rock skeleton, formation water, oil and gas, and mud in the volume model of remaining oil saturation logging, this study proposes a method for optimizing the capture section parameters based on the committee machine regression model. To begin, we select well sections from well logging data in a reasonable manner, allocate well sections with different component parameters in the same proportion, and construct sample datasets. The basic experts are then selected as multiple regression models, particle swarm optimization, and robust regression methods to train and learn the input parameters. By combining multiple experts, the regression committee machine improves the overall performance of the intelligent model. Finally, the genetic algorithm is used as a combiner to determine the contribution of each basic expert network in the final output, and the optimized parameters are obtained, which are then fed into the volume model to calculate the remaining oil saturation of the newly developed production wells, guiding the perforation and development work. The model is used to evaluate the remaining oil in the X oilfield, and the calculated water saturation matches the oil test results, proving the model’s accuracy and availability. The use of real-world data demonstrates that this method can effectively characterize the four parameter values in the volume model and provide reliable geophysical technical support for the evaluation of remaining oil.
Analysis and Implementation of Sliding Mode Controller-Based Variable Frequency Drive Using the SCADA System
Authors: Aljafari Belqasem , Ashok Kumar L. , Indragandhi V. , Subramaniyaswamy V.
Published: November 2022

Vector control of an asynchronous machine is traditionally accomplished by analogizing it to a separately excited DC machine. It provides decoupled torque and flux control that is perpendicular to each other, ensuring that neither vector interferes with the other. So, despite their close interconnection, torque and speed control are accomplished separately. The rotor flux is aligned with the direct axis of the synchronously rotating reference frame to achieve this. The PI controllers are critical in achieving the variable frequency drive (VFD) desired topology. The system employs three types of controllers: flux, speed, and torque. The flux controller is easy to tune, but the speed and torque controllers are more difficult to tune because the speed controller's output is the torque controller’s reference signal. Furthermore, there is no well-defined method for tuning the controllers in a vector control system. However, perfect tuning is required for the machine’s better dynamic behavior. It is clear from the above analysis that system identification is critical for tuning PI controllers. However, as an asynchronous machine, obtaining a decoupled system transfer function is extremely difficult. To solve this problem, the proposed system combines a seven-level pulse width modulation (PWM) inverter for vector control of a three-phase asynchronous nonstandard induction machine used in critical applications in nuclear power plants with a sliding mode control technique that eliminates the complexity of PI tuning. A second-order sliding mode controller could be used in the future to reduce the chattering and parameter variation effects. This controller can be enhanced with fuzzy logic principles to make it more robust and reliable, allowing it to be used in future drive designs for high-rating motors with critical applications.
An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in Magnetic Resonance Images
Authors: Eshmawi Ala’ A. , Khayyat Mashael , Algarni Abeer D. , Hilali-Jaghdam Inès
Published: November 2022

Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Presently, machine learning (ML) and deep learning (DL) models can be extremely utilized for disease detection and classification processes. Amongst the available approaches, the convolutional neural network (CNN) models have been widely used for computer vision and image processing issues such as ImageNet, facial detection, and digit classification. In this article, a novel computer aided diagnosis (CAD) based brain stroke detection and classification (CAD-BSDC) model has been developed for MRI images. The proposed CAD-BSDC technique aims in classifying the provided MR brain image as normal or abnormal. The CAD-BSDC technique involves different subprocesses such as preprocessing, feature extraction, and classification. Firstly, the input image undergoes preprocessing using adaptive thresholding (AT) technique for improving the image quality. Followed by, an ensemble of feature extractors such as MobileNet, CapsuleNet, and EfficientNet models are used. Besides, the hyperparameter tuning of the deep learning models takes place using the improved dragonfly optimization (IDFO) algorithm. Moreover, satin bowerbird optimization (SBO) based stacked autoencoder (SAE) is used for the classification of brain stroke. The design of optimal SAE using the SBO algorithm shows the novelty of the work. The performance of the presented technique was validated utilizing benchmark dataset which includes T2-weighted MR brain image collected from the axial axis with size of 256 × 256. The simulation outcomes indicated the promising efficiency of the proposed CAD-BSDC technique over the latest state of art approaches in terms of various performance measures.
RFID Scheme for IoT Devices Based on LSTM-CNN
Authors: Huang Kaizhi , Li Xinglu , Wang Shaoyu , Geng Zengchao , Niu Ge
Published: November 2022

As an essential branch of physical layer authentication research, radio frequency identification (RFID) has advantages in achieving lightweight and highly reliable authentication. However, in the Internet of Things (IoT) environment, where a large scale of devices are connected to the network, there is an issue that the difference of the RF fingerprints is less distinct among the same type of devices. To this end, in this paper, we propose an RFID scheme for IoT devices based on long-short term memory and convolutional neural network (LSTM-CNN). This scheme combines the excellent learning ability of LSTM and CNN to perceive the context information and extract the local feature of RF data. Specifically, RF data is first fed into LSTM to obtain long-term dependency features containing temporal information. Then, CNN is designed for secondary feature extraction to enlarge RF differences and further used for device classification. The experiment results on the open RF data set ORACLE indicate that the identification accuracy of the proposed scheme can reach over 99%. Compared with other schemes, the performance is improved by 6%-30%.
Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT
Authors: Peng Rongqun , Fu Xiuhua , Ding Tian
Published: November 2022

The high‐speed development of mobile broadband networks and IoT applications has brought about massive data transmission and data processing, and severe traffic congestion has adversely affected the fast‐growing networks and industries. To better allocate network resources and ensure the smooth operation of communications, predicting network traffic becomes an important tool. We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high‐speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR‐NLMS adaptive prediction method to perform time series prediction dataset transformation. Then, we propose a VSR‐LSTM algorithm for real‐time prediction of network traffic. Finally, compared with the traditional traffic prediction algorithm based on fixed sampling rate (FSR‐LSTM), we simulate the prediction accuracy of the VSR‐LSTM algorithm based on the variable sampling rate proposed. The experiment shows that VSR‐LSTM has higher traffic prediction accuracy because its sampling rate varies with the traffic.
[Retracted] Research on the Difference between Environmental Music Perception and Innovation Ability Based on EEG Data
Authors: Zhang Na
Published: November 2022

It is of great significance to practice and explore music creation for training creative talents. Perception includes feeling and perception, and feeling is a reflection of individual attributes of objective things directly acting on sensory organs. This paper mainly has a research on the difference between environmental music perception and innovation ability based on EEG data. First, this study performed noise reduction and artifact preprocessing of EEG signals generated by subjects with different levels of consciousness subjected to musical stimulation and then performed tensor decomposition to obtain the tensor component of EEG. The time‐domain components of these tensor components were analyzed together with five musical features (fluctuation centroid, fluctuation entropy, pulse clarity, key clarity, and mode), EEG tensor components related to music characteristics were analyzed, the power spectrum and the distribution of responsive brain regions were analyzed, and finally, the differences in the processing of music characteristics by different levels of consciousness were explored.
Analysis of the Effectiveness of Arytenoidectomy and Posterior Cordectomy with the Use of CFD Airflow Measurements in Patients with BVFP: A Retrospective Study
Authors: Gamrot-Wrzoł Marta , Marków Magdalena , Janecki Daniel , Orecka Bogusława , Warmuziński Krzysztof , Misiołek Maciej
Published: November 2022

Purpose. Bilateral vocal fold paralysis (BVFP) is a rare larynx disease manifested by dyspnea, which often requires surgical treatment. The aim of the study is to determine the effectiveness of unilateral arytenoidectomy with posterior cordectomy in the treatment of BVFP using the computational fluid dynamics (CFD) method. Methods. This study included 33 patients with BVFP who underwent unilateral laser arytenoidectomy with posterior cordectomy. Glottis area measurements and spirometry, as well as a self-assessment of respiratory efficiency were performed before the surgery and after the recovery period. Using the CFD method, computer models of the glottis were made. Then, changes in air pressure gradient and maximum air velocity at the level of glottis were calculated, and local fields of pressure and air velocities were obtained. Results. The values of glottal surface area (S), spirometry parameters (forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF)), inlet air velocity at the glottal level as well as patients self-assessment of respiratory efficiency turned out to be significantly higher after the operation. The values of maximum velocity at the glottal level, pressure gradient at the glottal level turned out to be significantly lower after the surgery. We observed that the greater the increase in glottal surface area, the greater the decrease in self-assessment scales (visual analogue scale (VAS) and Medical Research Council (MRC)). Increased levels of spirometry parameters after the surgery correlated with smaller decrease of PEF-dependent pressure gradient at the glottal level (PEFΔPCFD). Conclusion. Unilateral laser arytenoidectomy with posterior cordectomy is an effective method for the treatment of BVFP. CFD is a useful tool to determine and visualize the effectiveness of surgical treatment in BVFP.