bluuebunny/crossref_metadata_2025 huggingface dataset:
Complete Genome Sequences of Four
Parageobacillus
Strains Isolated from Soil in Japan
Authors: Miyazaki Kentaro , Hosoya Kae
Published: May 2022

We isolated four
Parageobacillus
strains from soil in Japan and completely sequenced their genomes. Three of four strains showed ≥98.9% average nucleotide identity (ANI) to
Parageobacillus caldoxylosilyticus
S1812
T
, while one strain, designated KH3-4, showed the highest ANI (91%) to
Parageobacillus thermantarcticus
M1
T
, suggesting the species novelty of KH3-4.
On Ramsey Numbers for Arbitrary Sequences of Graphs
Authors: Karas V. S. , Raigorodskii A. M.
Published: May 2022

In this work, we study nontrivial generalizations of Ramsey numbers to the case of arbitrary sequences of graphs. For many classes of sequences, exact values or asymptotics of Ramsey numbers are found.
Association between espresso coffee and serum total cholesterol: the Tromsø Study 2015–2016
Authors: Svatun Åsne Lirhus , Løchen Maja-Lisa , Thelle Dag Steinar , Wilsgaard Tom
Published: May 2022

Background

Coffee raises serum cholesterol because of its diterpenes, cafestol and kahweol, and the effect varies by brewing method. Population-based research on espresso coffee’s impact on serum cholesterol is scarce. Our aim was to examine how various brewing methods, in particular espresso, were associated with serum total cholesterol (S-TC).

Methods

We used cross-sectional population data from the seventh survey of the Tromsø Study in Northern Norway (N=21 083, age ≥40 years). Multivariable linear regression was used to assess the association between S-TC as the dependent variable and each level of coffee consumption using 0 cups as the reference level, adjusting for relevant covariates and testing for sex differences.

Results

Consumption of 3–5 cups of espresso daily was significantly associated with increased S-TC (0.09 mmol/L, 95% CI 0.01 to 0.17 for women and 0.16 mmol/L, 95% CI 0.07 to 0.24 for men), compared with participants drinking 0 cups of espresso per day. Consumption of ≥6 cups of boiled/plunger coffee daily was also associated with increased S-TC (0.30 mmol/L, 95% CI 0.13 to 0.48 for women and 0.23 mmol/L, 95% CI 0.08 to 0.38 for men), compared with participants drinking 0 cups of boiled/plunger coffee. Consumption of ≥6 cups of filtered coffee daily was associated with 0.11 mmol/L (95% CI 0.03 to 0.19) higher S-TC levels for women but not for men. Instant coffee consumption had a significant linear trend but showed no dose–response relationship when excluding participants not drinking instant coffee. There were significant sex differences for all coffee types except boiled/plunger coffee.

Conclusion

Espresso coffee consumption was associated with increased S-TC with significantly stronger association for men compared with women. Boiled/plunger coffee was associated with increased S-TC in both sexes and with similar magnitude as shown in previous research. Filtered coffee was associated with a small increase in S-TC in women. Further research on espresso and S-TC is warranted.
Evaluation of HZX-960, a novel DCN1–UBC12 interaction inhibitor, as a potential antifibrotic compound for liver fibrosis
Authors: Zhou Wenjuan , Dong Guanjun , Gao Ge , He Zhangxu , Xu Jiale , Aziz Shireen , Ma Liying , Zhao Wen
Published: May 2022

Liver fibrosis is a very common health problem and currently lacks effective treatments. Cullin RING E3 ligases (CRLs) regulate the turnover of ∼20% of mammalian cell proteins. Neddylation, the process by which NEDD8 is covalently attached to cullin proteins through sequential enzymatic reactions, is critical for the activation of CRLs and was recently found to be elevated in liver fibrosis. NEDD8-activating enzyme E1-specific inhibition led to the reduced liver damage characterized by decreased apoptosis, inflammation, and fibrosis. However, the relevance of a co-E3 ligase, DCN1, in liver fibrosis remains unclear. Here, a novel and potent DCN1–UBC12 interaction inhibitor HZX-960 was discovered with an IC50value of 9.37 nmol/L, which could inhibit the neddylation of cullin3. Importantly, we identified that HZX-960 treatment could attenuate transforming growth factor β-induced liver fibrotic responses by reducing the deposition of collagen I and α-smooth muscle actin, and upregulating cellular NF-E2-related factor 2, hemeoxygenase 1, and NADPH quinone oxidoreductase-1 levels in two hepatic stellate cell lines. Additionally, DCN1 was shown to be unregulated in CCl4-induced mice liver tissue, and liver fibrotic signaling in mice was reduced by HZX-960. Therefore, our data demonstrated that HZX-960 possessed anti-liver fibrosis ability and that DCN1 may be a potential therapeutic target for liver fibrosis treatment.
The path to proton structure at 1% accuracy
Authors: Ball Richard D. , Carrazza Stefano , Cruz-Martinez Juan , Del Debbio Luigi , Forte Stefano , Giani Tommaso , Iranipour Shayan , Kassabov Zahari , Latorre Jose I. , Nocera Emanuele R. , Pearson Rosalyn L. , Rojo Juan , Stegeman Roy , Schwan Christopher , Ubiali Maria , Voisey Cameron , Wilson Michael
Published: May 2022

We present a new set of parton distribution functions (PDFs) based on a fully global dataset and machine learning techniques: NNPDF4.0. We expand the NNPDF3.1 determination with 44 new datasets, mostly from the LHC. We derive a novel methodology through hyperparameter optimization, leading to an efficient fitting algorithm built upon stochastic gradient descent. We use NNLO QCD calculations and account for NLO electroweak corrections and nuclear uncertainties. Theoretical improvements in the PDF description include a systematic implementation of positivity constraints and integrability of sum rules. We validate our methodology by means of closure tests and “future tests” (i.e. tests of backward and forward data compatibility), and assess its stability, specifically upon changes of PDF parametrization basis. We study the internal compatibility of our dataset, and investigate the dependence of results both upon the choice of input dataset and of fitting methodology. We perform a first study of the phenomenological implications of NNPDF4.0 on representative LHC processes. The software framework used to produce NNPDF4.0 is made available as an open-source package together with documentation and examples.
A note on inflation in dRGT massive gravity
Authors: Afshar B. , Riazi N. , Moradpour H.
Published: May 2022

Although the dRGT massive gravity successfully explains the late-time cosmic acceleration, it cannot justify inflation. On the other hand, and in the frameworks of General Relativity and modified gravity, the interests and attempts to describe dark energy and inflation by using Lagranginas, which may have pole, have recently been enhanced. Subsequently, we are going to show that this kind of Lagrangian may justify inflation in the framework of dRGT massive gravity. The study is done focusing on the power and exponential potentials, and the results show a plausible consistency with the Planck 2018 data and its combination with BK18 and BAO.
The Task-Dependent Modular Covariance Networks Unveiled by Multiple-Way Fusion-Based Analysis
Authors: Jiang Lin , Li Fali , Chen Baodan , Yi Chanlin , Peng Yueheng , Zhang Tao , Yao Dezhong , Xu Peng
Published: May 2022

Cognitive processes induced by the specific task are underpinned by intrinsic anatomical structures with functional neural activation patterns. However, current covariance network analysis still pays much attention to brain morphologies or baseline activity due to the lack of an effective method for capturing the structural–functional covarying during tasks. Here, a multimodal covariance network (MCN) construction method was proposed to identify inter-regional covariations of the structural skeleton and functional activities by simultaneous magnetic resonance imaging and electroencephalogram (EEG). Results from two independent cohorts confirmed that MCNs could capture cognition-specific hierarchical modules in joint comprehensive multimodal features well, especially when time-resolved EEG was further integrated. The quantitative evaluation further demonstrates significantly larger modularity of MCN integrating fine-grained features from EEG. The application to the discovery cohort identified prominent modular covarying across the default mode and salience networks at rest, while the visual oddball task was accomplished by synchronous structural–functional cooperation within networks associated with attention control and working memory updating. Strikingly, the results of an external validation cohort showed a different covariant pattern corresponding to decision-specific cognitive modules. Overall, the results suggested that multimodal covariance analysis provides a reliable definition of multistate neural cognitive networks, further discloses modular-specific structural and functional co-variation.
Novel Brain Tumor Classification Model with MLPNN Using UNET
Authors: Vimala M. , Ranjith Kumar P.
Published: May 2022

The detection and assessment of brain tumor are crucial for medical diagnosis. The MRI study supports radiologists and surgeons in the development of strategies for patient care in medical imaging. Accurate classification and segmentation of abnormal tissue over normal tissue are a key element. The proposed research work focused on an efficient Brain Tumor Segmentation and Classification approach named Multi-Feature Frequency Similarity Multi-Layer Perceptron Neural Network (MFFS-MLPNN). The proposed model considers both low- and high-grade features of glioblastoma present in MR images and varying features of tumors according to their size, contrast and texture. The MRI brain image is preprocessed by applying contrast curvature-based iterative shearlet filter and histogram equalization technique for enhancement. Followed by preprocessing, the denoised image has been segmented using the U-NET segmentation methodology. After segmentation, the tumors are extracted by using the cross-multi-linear local embedding method. The extracted features including Gray–White (GW) features were trained with multi-layer perception neural network for classification. The tumor has been classified into malignant and benign based on the result of multi-layer perceptron neural network. Based on the value of MFFS value, the algorithm performs classification. The effectiveness of the proposed MFFS-MPLNN algorithms has been verified over BRATS 2018 and 2019 datasets and compared with other state-of-the-art techniques. The experimental results show that average dice score is 85.47% for BRATS 2018 dataset whereas average dice score is 75.4% for BRATS 2019. It is observed that the dice score value is increased from 73.3% to 85.47% for BRATS 2018 and 64.3% to 75.4% for BRATS 2019 dataset. The average Hausdorff distance value has been decreased from 9.6% to 4.98% for BRATS 2018 dataset and 14.3% to 6.24% for BRATS 2019 dataset. Similarly, the sensitivity of the proposed algorithm has been increased from 70.7% to 89% and 61% to 82.1% for both BRATS 2018 and BRATS 2019 datasets, respectively.
The Effect of Using Multi-Scroll Chaotic Systems on Chaos-Based Random Number Generators’ Performance
Authors: Çiçek Serdar
Published: May 2022

Chaotic and hyper-chaotic systems are used in various engineering applications such as encryption, communication, and artificial intelligence. Also, chaotic systems are widely used in chaos-based random number generator (RNG) designs as chaotic system signals are not periodic and produce different values continuously. Since multi-scroll chaotic systems (MSCSs) produce more than one scroll, the output values can take more different values than chaotic systems. In this study, the effects of different directional values and/or different numbers of scrolls of multi-scroll chaotic systems on chaos-based random number generators’ performance are investigated with NIST 800-22 (National Institute of Standards and Technology) and correlation coefficient tests. As a result of the research, it has been concluded that the use of multi-scroll chaotic systems with different directional values and/or different numbers of scrolls does not always have a direct positive effect on the performance of chaos-based random number generators. Thus, it is necessary to use a special pre-process method that will vary according to the multi-scroll chaotic system to be used for chaos-based RNG designs with good performance.
Analytical Delay Model and Stability Analysis for MLGNR Interconnects
Authors: Sharma Himanshu , Sandha Karmjit Singh
Published: May 2022

This paper analyzes the signal delay and relative stability of multilayer graphene nanoribbon (MLGNR) interconnects dependent on temperature at global lengths. A thermally aware driver interconnect load (DIL) system, constituted by equivalent single conductor (ESC) model along with mathematical equations, is proposed to convert multi-conductor transmission circuit into single transmission line of MLGNR. The temperature-dependent analytical delay model of MLGNR is evaluated and the obtained outcomes are compared with the simulated results. The analytical and simulated results are obtained at global interconnect length (2000-[Formula: see text]m) for 32[Formula: see text]nm, 22[Formula: see text]nm and 16[Formula: see text]nm nodes of technology under 200–500[Formula: see text]K temperature range of MLGNR. The simulation and analytical results reveal that the outcomes of the two models correspond well. The trend of the models shows the increase in delay with the rising temperature levels (200–500[Formula: see text]K) for three different nodes of technology. Further, relative stability analysis of MLGNR as interconnect line at three different technological nodes from 500–2000-[Formula: see text]m lengths is examined.
Digital Noise-Cancellation Circuit Implementation Using Proposed Algorithm and Karnaugh Map in a MASH 2-1 Delta-Sigma Modulator
Authors: Xiao Xiong , Huang Chong-Cheng , Sung Guo-Ming , Lee Chun-Ting
Published: May 2022

This paper presents the implementation of two digital noise-cancellation circuits (DNCCs) using a proposed algorithm and a Karnaugh map for a [Formula: see text] multistage noise-shaping (MASH) delta-sigma modulator (DSM). The MASH architecture inherits a superior signal-to-noise-and-distortion ratio (SNDR) with the aid of an efficient noise-cancellation technique either in the analogue or digital domain. The key motivation of this study was to design an area-efficient DNCC. The first approach employed a proposed algorithm (Algorithm-based DNCC) to implement the DNCC and to construct a delay block with an inverter and transmission gate. The second approach involved a Karnaugh map (K-map DNCC) and a delay block with a pair of D flip-flops. A maximum simulated signal-to-noise ratio of 135[Formula: see text]dB was completed with optimal analogue scaling coefficients for the proposed [Formula: see text] MASH DSM with DNCC. The simulated SNDRs of the Algorithm-based DNCC and K-map DNCC were 91.04[Formula: see text]dB and 91.16[Formula: see text]dB, respectively. Measured results show that the SNDR of the Algorithm-based DNCC, the SNDR of the K-map DNCC, power consumption and core area are approximately 58.7[Formula: see text]dB, 62.1[Formula: see text]dB, 0.26[Formula: see text][Formula: see text]W and 2275[Formula: see text][Formula: see text]m2, respectively, for the designed DNCCs with an operating frequency of 10.24[Formula: see text]MHz and supply voltage of 1.8[Formula: see text]V. The transistor counts of the Algorithm-based DNCC are 74 transistors, while they are 106 transistors for the K-map DNCC. The proposed Algorithm-based DNCC saves 32 transistors and approximately reduces its chip area to 69.8% of the K-map DNCC.
Tunneling analysis of Kerr–Newman black hole-like solution in Rastall theory
Authors: Ali Riasat , Bamba Kazuharu , Shah Syed Asif Ali , Saleem Muhammad Junaid
Published: May 2022

Hamilton–Jacobi ansatz is used to analyze boson charged particles tunneling in Rastall gravity Kerr–Newman black hole (BH) surrounded by perfect fluid matter through the horizon. The geometrical BH parameters are observed, how these are affecting the radiation utilizing the Lagrangian gravity equation. In this paper, the corrected Hawking temperature is computed by considering the effects of quantum gravity. In our analysis, the Rastall gravity BH solution surrounded by perfect fluid matter effects on Hawking radiation is analyzed graphically. Moreover, the stability and instability of Rastall gravity BH are investigated. The influence of quantum gravity and rotation parameters on BH radiation are also observed by graphical representation.
Are astrophysical “black” holes leaky?
Authors: Adler Stephen L.
Published: May 2022

We continue a study by Adler and Ramazanoǧlu (AR) of “black” holes as modified by a scale invariant dark energy action. For the spherically symmetric Schwarzschild-like case, AR found that there is no event horizon; hence spacetime is not divided by the “black” hole into causally disconnected regions. We review the formalism for locating trapped surfaces and apparent horizons, and show that the modified “black” hole has no trapped surfaces. Thus, one suspects that it is “leaky,” and that there will be a “black hole wind” of particles streaming out from the location of the nominal horizon. This will have astrophysical consequences, for example, the wind may feed and stabilize star formation in the vicinity of the “black” hole. We initiate a study of the stationary axially-symmetric rotating “black” hole as modified by a scale invariant dark energy action, i.e. one that is Kerr-like. To set-up the axial case, we note that the conserving completion of the dark energy stress-energy tensor can be calculated algebraically by solving a [Formula: see text] matrix equation. Using Mathematica we calculate and simplify the modified system of Einstein equations for the axial case in quasi-isotropic coordinates, and give appropriate boundary conditions. Solution of these equations, which may require developing a special purpose numerical program, is rendered tricky by a residual general coordinate invariance of quasi-isotropic coordinates. This leads to the interesting mathematical question of representing a positive planar function as the gradient squared of a harmonic function.
Study of shape change in T = 1 triplet 70Se, 70Br and 70Kr in microscopic–macroscopic approach
Authors: Nayak Satya Samiran , Mukherjee G.
Published: May 2022

The shapes of the A = 70, T = 1 triplet nuclei [Formula: see text]Se, [Formula: see text]Br and [Formula: see text]Kr have been investigated by Total Routhian Surface (TRS) calculations in the microscopic–macroscopic formalism. A Woods–Saxon potential with global set of parameters was used for obtaining the shell correction energy and the Strutinsky method has been applied to calculate the TRS. The [Formula: see text] and [Formula: see text] shape parameters, determined from the minimum of the TRS, show a structural change between mirror pairs [Formula: see text]Se and [Formula: see text]Kr. This is in support of the recent measurements performed at RIKEN.
SPATIOTEMPORAL DYNAMICAL ANALYSIS OF A PREDATOR–PREY SYSTEM WITH FEAR AND GROUP DEFENSE IN PREY
Authors: SHIVAM  , SINGH TEEKAM , KUMAR MUKESH
Published: May 2022

This paper considers a diffusive prey–predator system with fear and group defense in the prey population. Also, we consider that the mortality of predators is linear and quadratic. By using local stability analysis, we get the prerequisite of Turing instability. Using comprehensive numerical computations, we get non-Turing pattern formation in the system with linear death of predator. Turing patterns are obtained for the system with the quadratic death of the predator. The modeling technique of multiple scale analysis is used to determine amplitude equations near the Turing bifurcation origin for the model with the predator’s quadratic mortality rate. The amplitude equations stability leads to various Turing patterns such as spots, stripes, and mixed. The result focuses on changing the mortality rate linear to quadratic of a predator in the prey–predator system. The derived results support us in a more immeasurable understanding of prey–predator interaction dynamics in the actual world.
Combining Deep Neural Network and PLS-SEM to Predict Patients’ Continuity with Telemedicine
Authors: Zobair Khondker Mohammad , Sanzogni Louis , Houghton Luke , Islam Md. Zahidul
Published: May 2022

This study aims to adapt the Expectation Disconfirmation Theory and Technology Adoption Model to unveil provocative roles in patients’ satisfaction cognitions and subsequent continuity behaviors pertaining to telemedicine services in rural Bangladesh. A quantitative research model is developed and validated using a two-staged deep neural network and partial least squares structural equation modeling approach. The findings of this study provide evidence that five salient determinants; expectations, disconfirmation, performance, usefulness, and ease of use dominantly contribute to predicting patients’ satisfaction concerning continuity with telemedicine. This contributes to health informatics and behavioral literature by clarifying the complex interplay between patients’ satisfaction and determinants of continuity behavior in telemedicine’s domain. The findings provide novel insights into predictions of complex patients’ attitudes toward telemedicine continuity, and dynamic changes in adoption trends thereby assisting health professionals, global health experts, policymakers, and IS community in making higher quality informed decisions for people-centered future models of care.
Orthogonal Cutting Force Modeling Using the Nonequipartition Parallel Shear-Zone Distribution Model
Authors: Zheng Hualin , Xie Jinhong , Hu Teng , Fan Jia
Published: May 2022

This paper proposed a modified Oxley’s model using the nonequipartition parallel shear-zone theory. By introducing a strain and strain rate distribution model in the first shear band, the assumption that primary deformation zone was divided equally by the main shear plane was eliminated. Then the expressions of shear-zone thickness and resultant angle were derived according to Oxley’s cutting theory. Finally, a force prediction model for orthogonal cutting was established by analyzing the mechanical-thermal characteristics in the deformation zone. The thin-walled cylinder made of AISI 304 was used to carry out the orthogonal turning experiment. A variety of cutting force prediction models were used to compare with the experimental results. The calculation results from the proposed model show that the prediction accuracy of cutting force in both cutting and feed directions is improved.
Compact relativistic geometries in f(R, G) gravity
Authors: Rahman W. U. , Ilyas M. , Yousaf Z. , Ullah S. , Khan F. , Khan R.
Published: May 2022

One of the possible potential candidates for describing the universe’s rapid expansion is modified gravity. In the framework of the modified theory of gravity f(R, G), this work features the materialization of anisotropic matter such as compact stars. Specifically, to learn more about the physical behavior of compact stars, the radial, and tangential pressures as well as the energy density of six stars namely [Formula: see text], SAXJ1808.4-3658, 4U1820-30, PSR J 1614 2230, VELA X-1, and Cen X-3 are calculated. Herein, the modified theory of gravity f(R, G) is disintegrated into two parts i.e. the [Formula: see text] hyperbolic f(R) model and the three different f(G) models. The study focuses on graphical analysis of compact stars wherein the stability aspects, energy conditions, and anisotropic measurements are mainly addressed. Our calculation revealed that for the positive value of parameter n of the model f(G), all the six stars behave normally.
An Optimized Convolutional Neural Network for Multi-Spectral Change Detection Technique
Authors: Kumar L. Ashok , Jebarani M. R. Ebenezar
Published: May 2022

With the proliferation of multi-sensor remote sensing images, extraction of information has progressively concerned attention in recent years. It leads to great significant improvement in remote sensing areas for change detection techniques. Due to the advent of multi-spectral images in the change detection model, a change detection model named optimized convolutional neural network (CNN) framework is designed for monitoring land cover changes. This can assist to note the changes happening in the different periods. The proposed model is designed by integrating the normalization function and detection model by applying the deep learning (DL) algorithm. The actual experiment is carried out by taking the parameters accuracy and Kappa coefficient to show the effectiveness of the proposed model. The proposed experiment out performed well when compared to the existing mainstream techniques in multi-sensor images change detection. When compared with the existing DL techniques, the proposed design attained accuracy of 98.92% by possessing less loss function.
Diagnosing the Consequence of Uncertain Nutrient Deficiency, and its Sectionalization in Oryza Sativa Using Ensemble Learning Strategies
Authors: Sethumadhavi R. , Saleemdurai M. A.
Published: May 2022

Nutrition is an essential component in agriculture worldwide to assure high and consistent crop yields. The leaves frequently present signs of nutritional deficiencies in rice crops. A nutritional deficiency in the rice plant can also be diagnosed based on the leaf color and form. Image categorization is an effective and rapid method for analyzing such conditions. However, despite significant success in image classification, Ensemble Learning (EL) has remained elusive in paddy nutrition analysis. Ensemble learning is a technique for deliberately constructing and combining numerous classifier models to tackle a specific computational issue. In this work, we investigate the preciseness of several uncertain deep learning algorithms to detect nutritional deficits in rice leaves. Through soil and agricultural studies, around 2000 images of rice plant leaves were collected encompassing complete nutritional and about five divisions of nutrient deficiencies. The image proportion for learning via training, validation via evaluation, and testing phase were split into 4: 2: 2. For this, an EL method is chosen for the diagnosis and classification of nutritional deficits. Here, EL procedures are considered as a hybrid classification model that integrates CapsNET (Capsule network) and GCN (Graph Convolutional Neural) networks to evaluate the classification. The hybrid classification effectiveness was verified through color and lesion features which were compared with standard machine learning techniques. This research shows that EL strategies can effectively detect nutritional deficits in paddy. Furthermore, the suggested hybrid classification model achieved a better accuracy rate, along with sensitivity and specificity rates of 97.13%, 97.22%, and 96.47% correspondingly.
A Generalized Search Construct for Imperative Languages to Facilitate Declarative Programming
Authors: Smith James , Henderson Chris , Bansal Ajay
Published: May 2022

Search is one of the more common strategies used by problem-solving agents. For many hard problems, a backtracking search can be the most effective approach for finding a solution. Logic programming languages provide, as a core feature of the language, the mechanics for the search behind the scenes without requiring the user to specify the algorithm. However, this functionality has been generally left out of the core features of imperative programming languages. In this work, we propose integrating a search agent into an imperative programming language through a search construct in the syntax. By doing so, our intention is that the user may apply a declarative approach to problem solving. We extend the Go programming language with this search construct and develop an agent to perform the search. The algorithm behind the search is implicitly handled by the agent. We demonstrate the effectiveness of this search construct through solutions to an exhaustive search over a file-system, the NQueens problem, and the monkey and the bananas problem. To evaluate the performance of our agent, we compare its runtime performance against two of the common Prolog implementations, SWI-Prolog and GNU Prolog.
Flexible-Joint Manipulator Trajectory Tracking with Two-Stage Learned Model Utilizing a Hardwired Forward Dynamics Prediction
Authors: Pavlichenko Dmytro , Behnke Sven
Published: May 2022

Flexible-joint manipulators are frequently used for increased safety during human–robot collaboration and shared workspace tasks. However, joint flexibility significantly reduces the accuracy of motion, especially at high velocities and with inexpensive actuators. In this paper, we present a learning-based approach to identify the unknown dynamics of a flexible-joint manipulator and improve the trajectory tracking at high velocities. We propose a two-stage model which is composed of a one-step forward dynamics future predictor and an inverse dynamics estimator. The second part is based on linear time-invariant dynamical operators to approximate the feed-forward joint position and velocity commands. We train the model end-to-end on real-world data and evaluate it on the Baxter robot. Our experiments indicate that hard-wiring the one-step forward dynamics prediction into the model improves the performance, compared to the same model without prediction. We compare joint position, joint velocity and end-effector position tracking accuracy against the classical baseline controller and several simpler models with payloads of variable weight.
A Non-parametric Bayesian Learning Model Using Accelerated Variational Inference on Multivariate Beta Mixture Models for Medical Applications
Authors: Manouchehri Narges , Bouguila Nizar
Published: May 2022

Clustering as an exploratory technique has been a promising approach for performing data analysis. In this paper, we propose a non-parametric Bayesian inference to address clustering problem. This approach is based on infinite multivariate Beta mixture models constructed through the framework of Dirichlet process. We apply an accelerated variational method to learn the model. The motivation behind proposing this technique is that Dirichlet process mixture models are capable to fit the data where the number of components is unknown. For large-scale data, this approach is computationally expensive. We overcome this problem with the help of accelerated Dirichlet process mixture models. Moreover, the truncation is managed using kd-trees. The performance of the model is validated on real medical applications and compared to three other similar alternatives. The results show the outperformance of our proposed framework.
Prediction of Parkinson’s disease based on feature selection and classification of dopamine transporter scan of brain using deep learning architectures
Authors: Sathya Bama B. , Bevish Jinila Y.
Published: May 2022

Parkinson’s disease (PD) is a neurological disease that produces uncontrollable movements and a variety of other symptoms. It can be difficult to make an accurate PD diagnosis since the signs and symptoms, especially early on, might be mistaken for other medical diseases or physiological changes associated with normal aging. This research proposed novel technique in predicting PD based on dopamine transporter scan (DaTscan) images of brain using deep learning techniques. Here the aim is to collect the historical data and live DaTscan image of patients with symptoms of PD and predict disease. Initially input data have been pre-processed for image resize, noise removal and smoothening. Then the processed image has been selected based on their features using kernel-based deep convolution neural network (KDCNN). The selected deep features have been classified using reinforcement Q-learning-based neural networks (RQLNNs) to predict the presence of PD. Here experimental results show feature-selected and classified output of DaTscan brain image using the proposed model. For MRI image dataset, the proposed technique obtained accuracy of 97.5%, precision of 93%, recall of 82% and F-1 score of 87%. The proposed technique obtained accuracy of 98%, precision of 93%, recall of 80% and F-1 score of 88% for DaTscan dataset.
Dynamics of COVID-19 epidemic via two different fractional derivatives
Authors: Kumar Pushpendra , Erturk Vedat Suat , Govindaraj V. , Inc Mustafa , Abboubakar Hamadjam , Nisar Kottakkaran Sooppy
Published: May 2022

In December 2019, the novel Coronavirus, also known as 2019-nCoV or SARS-CoV-2 or COVID-19, was first recognized as a deadly disease in Wuhan, China. In this paper, we analyze two different nonclassical Coronavirus models to observe the outbreaks of this disease. Caputo and Caputo–Fabrizio (C–F) fractional derivatives are considered to simulate the given epidemic models by using two separate methods. We perform all required graphical simulations with the help of real data to demonstrate the behavior of the proposed systems. We observe that the given schemes are highly effective and suitable to analyze the dynamics of Coronavirus. We find different natures of the given model classes for both Caputo and C-F derivative sense. The main contribution of this study is to propose a novel framework of modeling to show how the fractional-order solutions can describe disease dynamics much more clearly as compared to integer-order operators. The motivation to use two different fractional derivatives, Caputo (singular-type kernel) and Caputo–Fabrizio (exponential decay-type kernel) is to explore the model dynamics under different kernels. The applications of two various kernel properties on the same model make this study more effective for scientific observations.
On the existence and uniqueness analysis of fractional blood glucose-insulin minimal model
Authors: Dubey Ravi Shanker , Goswami Pranay , Baskonus Haci Mehmet , Tailor Gomati A.
Published: May 2022

In our research work, we suggest the modified minimal model of fractional order and analyze it using the homotopy decomposition method (HDM). The minimal model is quite a useful mathematical model which describes the behavior of glucose-insulin metabolism. The original model was given in the 80s and has been updated over a different period. In this modified model, we add a one-factor diet which plays an important role in the blood-glucose analysis. We obtained the numerical results by using the homotopy decomposition method. HDM is extremely useful, significant, and very simple. We also discuss the existence and uniqueness of the fractional model.
Emerging transportation innovations: Promises and pitfalls
Authors: Labi Samuel , Sinha Kumares C.
Published: May 2022

Rapid growth in information and communication technologies has spawned a number of major innovations in transportation area, including automation and connectivity. At the same time, the advancement in battery technology has accelerated the electrification of transportation vehicle propulsion. This paper, focusing on highway-oriented surface transportation, examines the current development of these innovations, along with their synergies, benefits, pitfalls, trends, possible barriers to deployment, and wider impacts.
Trust Measurement in Human-Autonomy Teams: Development of a Conceptual Toolkit
Authors: Krausman Andrea , Neubauer Catherine , Forster Daniel , Lakhmani Shan , Baker Anthony L. , Fitzhugh Sean M. , Gremillion Gregory , Wright Julia L. , Metcalfe Jason S. , Schaefer Kristin E.
Published: May 2022

The rise in artificial intelligence capabilities in autonomy-enabled systems and robotics has pushed research to address the unique nature of human-autonomy team collaboration. The goal of these advanced technologies is to enable rapid decision-making, enhance situation awareness, promote shared understanding, and improve team dynamics. Simultaneously, use of these technologies is expected to reduce risk to those who collaborate with these systems. Yet, for appropriate human-autonomy teaming to take place, especially as we move beyond dyadic partnerships, proper calibration of team trust is needed to effectively coordinate interactions during high-risk operations. But to meet this end, critical measures of team trust for this new dynamic of human-autonomy teams are needed. This article seeks to expand on trust measurement principles and the foundation of human-autonomy teaming to propose a “toolkit” of novel methods that support the development, maintenance, and calibration of trust in human-autonomy teams operating within uncertain, risky, and dynamic environments.
Detection of Stress Hormone with Semiconducting Single-Walled Carbon Nanotube-Based Field-Effect Transistors
Authors: Bian Long , Shao Wenting , Liu Zhengru , Zeng Zidao , Star Alexander
Published: May 2022

Non-invasive detection and quantification of the stress hormone cortisol not only provide the assessment of stress level but also enable close monitoring of mental and physical health. In this work, we report two types of field-effect transistors (FETs) based on semiconducting single-walled carbon nanotubes (sc-SWCNTs) as selective cortisol sensors. In one FET device configuration cortisol antibody is directly attached to sc-SWCNTs, the other one is using gold nanoparticles (Au NPs) as linkers in between antibody and sc-SWCNTs to enhance the device conductance. We fabricated and characterized both device configurations to investigate how the nanomaterial interface to cortisol antibody influences the biosensor performance. We tested the sensors in artificial sweat and compared these two types of sensors in terms of limit of detection and sensitivity, and the results indicate that direct binding between antibody and sc-SWCNTs yields better biosensor characteristics.
Proton-Electron Hole Interactions in Sr(Ti,Fe)O3−δ
Mixed-Conducting Perovskites
Authors: Miruszewski T. , Skubida W. , Winiarz P. , Dzierzgowski K. , Mielewczyk-Gryń A. , Wachowski S. , Gazda M.
Published: May 2022

Oxides in which total electrical conductivity is determined by the partial conductivities of three types of charge carriers i.e. holes/electrons, oxygen ions, and protons are key components of well-functioning proton ceramic fuel cells. Apart from electrochemical properties also easily modified microstructure is an important feature of the electrodes. In the paper, a group of SrTi1−xFexO3−δ
(STF, x = 0.2–0.8) perovskites prepared by solid-state synthesis was studied. The microstructure of ceramics was studied using SEM, and the results showed a significant influence of Fe amount on the grain size and material’s porosity. The electrical transport results show a different conductivity in a dry and humidified atmosphere. The hydration kinetics related to the water incorporation are presented with the transition from single-fold to two-fold behaviour, related to the proton-electron hole interaction observed in measurements. The obtained results strictly show a triple-conductivity of SrTi0.8Fe0.2O3−δ
system at a lower temperature range, where the protonic defects are thermodynamically stable.
Improvement of BBRv2 Congestion Control Algorithm Based on Flow-aware ECN
Authors: Pan Wansu , Tan Haibo , Li Xiaofeng , Xu Jinlin , Li Xiru
Published: May 2022

Google proposed a new congestion control algorithm (CCA) based on bottleneck bandwidth and round-trip propagation time (BBR), which is considered to open a new era of congestion control. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. The BBR v2 algorithm is a recently updated version by Google, which aims to improve some of the problems in the original BBR (BBRv1) algorithm, such as interprotocol fairness issues, RTT fairness issues, and excessive retransmissions. The BBRv2 evaluation results show that it can improve the coexistence with the loss_based algorithm and alleviate some of the shortcomings in BBRv1. However, when multiple BBRv2 flows enter the same link at different times, fair convergence cannot be achieved, and RTT fairness still exists. Based on these problems, we analyze the root cause and proposed an improved algorithm BBRv2+, which uses flow-aware explicit congestion notification (ECN) to quantify queue information and feedback on the accurate congestion degree. BBRv2+ algorithm can avoid blind window constraints and selectively mark packets so that different flows can converge to fairness. In the simulation experiment of Network Simulator 3 (NS3), the results show that the BBRv2+ algorithm can improve intraprotocol fairness and RTT fairness and ensure bandwidth utilization and interprotocol fairness.
High-Concentration Metformin Reduces Oxidative Stress Injury and Inhibits the Growth and Migration of Clear Cell Renal Cell Carcinoma
Authors: Liu Yang , Li Jianwei , Song Miaomiao , Qi Guisong , Meng Lingling
Published: May 2022

Objective. To explore the mechanism of metformin in treating CCRCC. Methods. Prospective cohort study was conducted. SOD and cyclin D in six CCRCC samples donated by volunteers were detected to compare the degree of oxidative stress injury and the status of cell proliferation. 786-0 CCRCC cells were cultured in vitro with different concentrations of metformin, and MTT assay and Transwell cell migration and wound healing assay were used to detect their proliferation and migration. After culture, SOD and cyclin D in 786-0 CCRCC cells were also detected. Results. In the edge tissue, SOD was lower than in the tumor nest and normal tissue, and cyclin D was highly expressed. In grade II CCRCC, SOD was higher than in grade IV CCRCC, but cyclin D was also highly expressed in grade IV CCRCC. The cell proliferation rate and density of the metformin group were lower than the control group, while in the high-concentration metformin group, it was lower than medium- and low-concentration groups. After culture, the migration of 786-0 cells in the metformin group was significantly lower than that in the control group, the wound healing rate was decreased, and the migration and wound healing rates in the high-concentration metformin group were significantly lower than those in the medium- and low-concentration groups. However, the SOD of the metformin group was higher than the control group, but the cyclin D was lower, while the SOD was higher than medium- and low-concentration groups in the high-concentration group, but the cyclin D was lower after cultured. Conclusion. High-concentration metformin can reduce oxidative stress injury, increase the expression of SOD in CCRCC, and reduce cyclin D in CCRCC to inhibit proliferation and migration, which has optimistic prospects and application value in controlling the progression of CCRCC.
Analysis of Home-School Collaboration Mechanism and Psychological Impact Based on MultiSource Big Data Information Fusion
Authors: Li Yaling
Published: May 2022

Affected by the global epidemic, teachers and students need to study related courses online, which have become a new inevitable trend for the construction of online courses. Introduce new technologies and apply mature big data to the field of education. Through information fusion processing of multisource data, we can effectively integrate educational resources and improve educational level. The research results of this article show that (1) different identity roles have different needs and problems for home-school collaboration, which needs to be designed from reality. (2)

P
<
0.01

indicates that there are significant differences in parents’ evaluation in different areas;

P
>
0.05

indicates that parents’ evaluation has nothing to do with gender, and there is no overall difference. (3) Compared with BPNDA and SOFMDA, CNNMDA has the advantages of slow growth of error rate, high efficiency of data fusion, and 7.5% reduction of energy consumption of network nodes, showing excellent performance. (4) There is no abnormality in the function of user portrait after detection, the expected effect is over 80%, and the concurrent times per second are about 441 times. The final result of the experiment is very good. Meet our design and expectations. The follow-up work can also optimize the details.
Cloud Storage Data Access Control Scheme Based on Blockchain and Attribute-Based Encryption
Authors: Yang Xiaodong , Chen Aijia , Wang Zhisong , Li Shudong
Published: May 2022

Cloud storage is a popular model of the application in various fields, and the security of storage data and access permission have been widely considered. Attribute-based encryption (ABE) provides fine-grained user access control and ensures data confidentiality. However, current ABE access control schemes rely on trusted cloud servers and provide a low level of security. To solve these problems of traditional encryption schemes, we propose a blockchain-based and ABE cloud storage data access control scheme. In this article, blockchain and smart contract technology are the core elements to ensure data integrity and build a decentralized verification method for outsourcing results. This application can minimize the reliance on servers in the cloud environment. Based on the ciphertext-policy ABE algorithm, the proposed scheme supports a hidden access policy to avoid the risk of privacy leakage. In addition, we adopt outsourcing technology and predetected decryption algorithms to reduce the computational overhead of local and outsourced servers. Security analysis and performance evaluation show that our proposed scheme has high computational efficiency and satisfies the condition of indistinguishability under the chosen-ciphertext attacks.
Characteristic Investigations on Ethiopian Kaolinite: Effect of Calcination Temperature on Pozzolanic Activity and Specific Surface Area
Authors: Kassa Adamu Esubalew , Shibeshi Nurelegne Tefera , Tizazu Belachew Zegale , Prabhu S. Venkatesa
Published: May 2022

Kaolinite, a clay mineral, is an important industrial mineral which was gainfully utilized with several industrial significances. Towards mineral beneficiation, the present study aimed to investigate the effect of calcination temperature on the pozzolanic activity and specific surface area of Ethiopian kaolinite (Ek). The raw Ek and calcined Ek were characterized using different sophisticated techniques, such as XRD, FTIR, DSC, TGA, dynamic light scattering, electrophoretic light scattering, and SEM analysis. The pozzolanic activity and specific surface area of Ek particles were ascertained using Chapelle’s and Brunauer–Emmett–Teller methods, respectively, with some modifications. The results revealed that the calcination temperature had a great influence on pozzolanic activity and specific surface area of Ek. It was observed that the pozzolanic activity linearly increased with calcination temperature until 700°C and showed a maximal value of 1235 mg Ca(OH)2 g−1. On the other hand, beyond 700oC, the pozzolanic activity was inferred to be declined to 445 mg Ca(OH)2 g−1 at 1000°C due to agglomeration of calcined kaolinite (metakaolinite) and formation of spinel phase. A similar phenomenon was observed for specific surface area which increased with temperature until 700°C to exhibit the maximal values of 47.794 m2·g−1. Further, the investigation on zeta potential analysis showed that the magnitude value of the metakaolinite was observed to be less than the zeta potential value of the raw kaolinite, which indicated that the flocculation tendency of metakaolinite was greater than the kaolinite flocculation affinity in water.