bluuebunny/crossref_metadata_2025_split huggingface dataset:
Angular and linear measurements of adult flexible flatfoot via weight-bearing CT scans and 3D bone reconstruction tools
Authors: Ortolani Maurizio , Leardini Alberto , Pavani Chiara , Scicolone Silvia , Girolami Mauro , Bevoni Roberto , Lullini Giada , Durante Stefano , Berti Lisa , Belvedere Claudio 
Published: August 2021

AbstractAcquired adult flatfoot is a frequent deformity which implies multiple, complex and combined 3D modifications of the foot skeletal structure. The difficult thorough evaluation of the degree of severity pre-op and the corresponding assessment post-op can now be overcome by cone-beam (CBCT) technology, which can provide access to the 3D skeletal structure in weight-bearing. This study aims to report flatfoot deformities originally in 3D and in weight-bearing, with measurements taken using two different bone segmentation techniques. 21 such patients, with indication for surgical corrections, underwent CBCT (Carestream, US) while standing on one leg. From these scans, 3D models of each bone of the foot were reconstructed by using two different state-of-the-art segmentation tools: a semi-automatic (Mimics Innovation Suite, Materialise, Belgium), and an automatic (Bonelogic Ortho Foot and Ankle, Disior, Finland). From both reconstructed models, Principal Component Analysis was used to define anatomical reference frames, and original foot and ankle angles and other parameters were calculated mostly based on the longitudinal axis of the bones, in anatomical plane projections and in 3D. Both bone model reconstructions revealed a considerable valgus of the calcareous, plantarflexion and internal rotation of the talus, and typical Meary’s angles in the lateral and transverse plane projections. The mean difference from these angles between semi-automatic and automatic segmentations was larger than 3.5 degrees for only 3 of the 32 measurements, and a large number of these differences were not statistically significant. CBCT and the present techniques for bone shape reconstruction finally provide a novel and valuable 3D assessment of complex foot deformities in weight-bearing, eliminating previous limitations associated to unloaded feet and bidimensional measures. Corresponding measurements on the bone models from the two segmentation tools compared well. Other more representative measurements can be defined in the future using CBCT and these techniques.
Sea-level trends across The Bahamas constrain peak last interglacial ice melt
Authors: Dyer Blake , Austermann Jacqueline , D’Andrea William J. , Creel Roger C. , Sandstrom Michael R. , Cashman Miranda , Rovere Alessio , Raymo Maureen E. 
Published: August 2021

Significance
The seas are rising as the planet warms, and reconstructions of past sea level provide critical insight into the sensitivity of ice sheets to warmer temperatures. Past sea level is reconstructed from the geologic record by measuring the elevations of fossilized marine sediments and coral reefs. However, the elevations of these features also record local uplift or subsidence due to the growth and decay of ice sheets since the time of deposition. We compare observations of paleo sea level across the Bahamian archipelago with a range of Earth deformation models to revise estimates of the last interglacial global mean sea level. Our results suggest that polar ice sheets may be less sensitive to high-latitude warming than previously thought.
Targeted in situ cross-linking mass spectrometry and integrative modeling reveal the architectures of three proteins from SARS-CoV-2
Authors: Slavin Moriya , Zamel Joanna , Zohar Keren , Eliyahu Tsiona , Braitbard Merav , Brielle Esther , Baraz Leah , Stolovich-Rain Miri , Friedman Ahuva , Wolf Dana G. , Rouvinski Alexander , Linial Michal , Schneidman-Duhovny Dina , Kalisman Nir 
Published: August 2021

Significance
We present a generic methodology that extracts structural data from living, intact cells for any protein of interest. Application of this methodology to different viral proteins resulted in significant cross-link sets that revealed the connectivity within their structures. Importantly, we show that these cross-link sets are detailed enough to enable the integrative modeling of the full-length protein sequence. Consequently, we report the global structural organization of Nsp2 and the dimer of the nucleocapsid protein. We foresee that similar applications will be highly useful to study other recalcitrant proteins on which the mainstream structural approaches currently fail.
On the proximate Kitaev quantum-spin liquid α-RuCl3: thermodynamics, excitations and continua
Authors: Loidl A , Lunkenheimer P , Tsurkan V 
Published: August 2021

Abstract
This topical review provides an overview over recent thermodynamic, infrared, and THz results on the proximate Kitaev spin-liquid. Quantum-spin liquids are exotic phases characterized by the absence of magnetic ordering even at the lowest temperatures and by the occurrence of fractionalized spin excitations. Among those, Kitaev spin liquids are most fascinating as they belong to the rare class of model systems, that can be solved analytically by decomposing localized spins S = 1/2 into Majorana fermions. The main aim of this review is to summarize experimental evidence obtained by THz spectroscopy and utilizing heat-capacity experiments, which point to the existence of fractionalized excitations in the spin-liquid state, which in α-RuCl3 exists at temperatures just above the onset of magnetic order or at in-plane magnetic fields just beyond the quantum-critical point where antiferromagnetic order becomes suppressed. Thermodynamic and spectroscopic results are compared to theoretical predictions and model calculations. In addition, we document recent progress in elucidating the sub-gap (<1 eV) electronic structure of the 4d
5 ruthenium electrons to characterize their local electronic configuration. The on-site excitation spectra of the d electrons below the optical gap can be consistently explained using a spin–orbit coupling constant of ∼170 meV and the concept of multiple spin–orbital excitations. Furthermore, we discuss the phonon spectra of the title compound including rigid-plane shear and compression modes of the single molecular layers. In recent theoretical concepts it has been shown that phonons can couple to Majorana fermions and may play a substantial role in establishing the half-integer thermal quantum Hall effect observed in this material.
Generalized spaces of pointwise regularity: toward a general framework for the WLM
Authors: Loosveldt L , Nicolay S 
Published: August 2021

Abstract
In this work we generalize the spaces

T

u

p

introduced by Calderón and Zygmund using pointwise conditions emanating from generalized Besov spaces. We give conditions binding the functions belonging to these spaces and their wavelet coefficients. Next, we propose a multifractal formalism based on such spaces which generalizes the so-called wavelet leaders method and show that it is satisfied on a prevalent set.
On 2-Domination Number of Some Graphs
Authors: Maheswari V. , Anantha Kiruthika N.H. , Nagarajan A. 
Published: August 2021

Abstract
Domination and 2-domination numbers are defined only for graphs with non-isolated vertices. In a Graph G = (V, E) each vertex is said to dominate every vertex in its closed neighborhood. In a graph G, a subset S of V(G) is called a 2-dominating set of G if every vertex in v ∈ V, is in V-S and has atleast two neighbors in S. The smallest cardinality of a 2-dominating set of G is known as the 2-domination number γ2(G). In this paper, we find 2-dominating set of some special graphs and also find the 2-domination number of graphs.
The Lattice Structure of the Subgroups of order 21 in the subgroup lattices of 3 X 3 Matrices over Z2
Authors: Durai Murugan V. , Seethalakshmi R. 
Published: August 2021

Abstract
Let 𝓖 be the set of all 3 X 3 non-singular matrices

(

a

b

c

d

e

f

g

h

i

)

, where a, b, c, d, e, f, g, h, i are integers modulo p. Then 𝓖 is a group under matrix multiplication modulo p, of order (p

n
− 1) (p

n
-p) (p

n
-p
2) … … (p

n
-p

n
−1). Let G be the subgroup of 𝓖 defined by

G
=

{

(

a

b

c

d

e

f

g

h

i

)

∈
G
:

|

a

b

c

d

e

f

g

h

i

|

=
1
}

. Then G is of order

(

p

n

−
1
)

(

p

n

−
p
)

(

p

n

−

p

2

)

…
…

(

p

n

−

p

n
−
1

)

p
−
1

. Let L(G) be the lattice formed by all subgroups G. In this paper, we give the structure of the subgroups of order 21of L(G) in the case when P=2.
Statistical entropy of a class of regular black holes by brick Wall Model Journal of Physics: Conference Series
Authors: Singh D V , Singh B K 
Published: August 2021

Abstract
In the paper we study the entropy of the scalar field in the background of regular black holes (Heyward and nonsingular)by using the brick-wall method suggested by the’t Hooft. We show the leading term of entropy follows the Bekenstein-Hawking area law and the brickwall method predicts the corrections to the entropy of the regular black hole. The general structure of the coefficient of logarithmic sub-leading corrections for the both black holes are same. The coefficients of the logarithmic corrections are-1/180 for both regular black holes which differ from the Schwarzschild black hole.
Regular Property on Product of Pythagorean Anti Fuzzy Graph
Authors: Maheswari V. , Syedul Fahima U. , Bala Deepa Arasi K. 
Published: August 2021

Abstract
In this paper, we introduce the notion of Pythagorean Anti fuzzy graph. We then define the Cartesian product and Lexicographic product on Pythagorean Anti fuzzy graph. It is proved that Cartesian product of two Pythagorean Anti fuzzy graphs is Pythagorean Anti fuzzy graph and Lexicographic product of two Pythagorean Anti fuzzy graphs is Pythagorean Anti fuzzy graph. In general, Cartesian product and lexicographic product of two regular Pythagorean Anti fuzzy graphs need not be Pythagorean Anti fuzzy graph. Necessary and sufficient conditions for Cartesian and lexicographic product of two Pythagorean Anti fuzzy graphs to be regular are determined. Further, we define the concept of isomorphism on Pythagorean Anti fuzzy graph with suitable example.
Effect of TD viscosity and Coriolis force on thermohaline convection of ferrofluid in Darcy porous matrix: A Galerkin numerical technique
Authors: Ratchagar Nirmala P , Seyalmurugan S. 
Published: August 2021

Abstract
The effect of TD viscosity (Temperature Dependent viscosity) and Coriolis force in horizontal ferromagnetic fluid layer with Darcy porous matrix is investigated theoretically. This fluid layer is heated from below and it is salted from above. The non-linear behavior is omitted by use of a linear stability theory. The normal mode technique is taken to account to investigate the convective system with free-free boundaries. To investigate the entire system the exact solution is obtained. The stationary instability is obtained. But, oscillatory instability can not occur in the study. The critical thermal magnetic Rayleigh number N

S
C
is calculated for sufficient large values of M
1. In the analysis, we have attempted to analyze T

a
, V and D
a on the system in many situations.
Performance Estimation of Adaptive Fuzzy Logic Speed Controller for Reduced Switch BLDC Motor Drive
Authors: Krishna Veni K.S. , Senthil Kumar N. , Gnanavadivel J. 
Published: August 2021

Abstract
This article deals with the implementation of an adaptive fuzzy logic based reduced switch brushless DC motor drive. In topological view, the cost of the system chiefly depends upon the power semiconductor switches. Hence switch reduction concept is adapted in this paper. The operating principle, switching sequence formulation of reduced switch topology is presented. An intelligent system for reduced switch BLDC drive is designed and tested for different load conditions. Simulation results as well as comparative performance with conventional controllers are presented and discussed. The parameters such as rise time and steady state error are observed. The effectiveness and robustness of the reduced switch BLDC drive for variable speed applications is demonstrated. The rise time and steady state error of the proposed system is improved about an average of nearly 60% and 30% respectively compared to other controllers.
Experimental Studies on Portal Frames made with Palm oil Shell Light Weight Fiber Reinforced Concrete
Authors: Kumar Jagarapu Durga Chaitanya , Eluru Arunakanthi 
Published: August 2021

Abstract
This present research mainly focused on the behaviour of monolithically and precast portal frames made with lightweight fiber reinforced concrete. The lightweight fiber reinforced concrete (LWFRC) is achieved with Palm oil shells (POS) and glass fibers (G.F). The POS is replaced with coarse aggregates and the G.F is added to the concrete to increase ductility parameters. To reduce the further density of the concrete, replaced cement with GGBS and Palm Oil Fuel Ash (POFA) individually. All these replacements are helping the reduction of solid waste and greenhouse gases from cement Industries. Portal frames are made with M30 Grade LWFRC through replacements. All the precast frames are connected with the Dowel bar connection through the grouting. All the LWFRC frames are compared with conventional concrete frames. The palm oil shells are replaced up to 50% in coarse aggregates to diminish the density of the concrete and also to decrease the greenhouse gases from the cement industry; cement is replacing with Ground Granulated Blast Furnace Slag (GGBS) and Palm Oil Fuel Ash (POFA).
Maximization of Cr Removal in Continuous Counter-current Liquid-Solid Fluidized Bed: A Machine Learning Approach
Authors: Datta Meghna , Ansari Muzammil H , Bandyopadhyay Subhrajyoti , Selvam K. , Sam David S. 
Published: August 2021

Abstract
Continuous counter-current liquid solid (CCLS) fluidized bed is used as an adsorption column. Due to the counter current contacting pattern and easiness in handling the fresh as well as used adsorbent continuously, CCLS adsorption column can be preferred over packed adsorption column. In the present work experimental data for chromium removal from the wastewater using CCLS adsorption has been used for developing models using artificial neural network (ANN), a well-known machine learning technique. The percentage removal of Cr is mapped as function of liquid velocity, solid velocity, particle diameter, initial concentration, and height of the column through ANN. The developed ANN model is used as objective function for the design of the process using genetic algorithm (GA), a metaheuristic tool for optimization. The results provide specific guidelines for achieving optimum Cr removal.
Automated Detection of Lung Cancer Based on Neuro Fuzzy Technique
Authors: Camy Joshya Y , Divyabharathi P , Archana P , Vikram N 
Published: August 2021

Abstract
Our human civilization has encountered many dangerous diseases and death due to lung cancer is increasing worldwide. The finding of lung cancer cells in the earliest stage is the most challenging process in the medical field. To address the issue, digital image processing techniques, along with neuro-fuzzy logic, has experimented in this current study. The system is aimed to help in the initial stage detection of lung nodules by classifying the existence of abnormalities in the computed tomography (CT) scan image. The critical stages involved in the detection process are preprocessing, image enhancement, image segmentation, feature extraction and neuro-fuzzy algorithm. The proposed system is an entirely functional automatic method which completely avoids physical calculation and this designed system produces a better result with a high accuracy rate.
Acoustic Anthropogenic Noise Reduction In Pixel Priority Based Algorithm
Authors: Rajkamal N , Jothi lakshmi G.R. 
Published: August 2021

AbstractPriority pixel enhancement for removing hindrances in acoustics. In this paper underwater sound speed gradient monitoring systems grieved in various conditions data monitoring. Compress and decompress sound waves of water molecules travel through underwater makes monitoring hindrances to ocean monitoring sensors produce dithered image data retrieving is based on pixel based algorithm to get the original point of data legible to enhance the part of the image in the priority enhancement methods in under water sound monitoring systems.
Hamburger Stand Support System Using Supervised Machine Learning Algorithms
Authors: Jeyabharathi J. , Sakthivel V. , Sivaramakrishnan R. 
Published: August 2021

AbstractSupport System approaches form a pattern from a user’s over efforts as well as related conclusion created by other users. This form is then used to voting items that the user may have an interest in the particular product. It can propose the hamburger stand to the user based on the previous procure, voting and reports. Machine learning plays a vital role in hamburger stand support system, which process the user based on their old events. Here, hamburger stand support can be performed by reports, voting, reviews and restaurant reputation, tastes, which is given by the other users. This current support system typically combines one or more approaches into a hybrid system. This paper suggest a hybrid system for classifying the users by using the machine learning algorithms such as Naive Bayes and Support Vector Machine(SVM). It will improve the nourishment transaction, decreases the amount of work and imparts the user fulfillment.
Machine Learning based Fitness application using BMI value
Authors: Jeyaranjani J. , Kapoor Utkash 
Published: August 2021

AbstractOne of the foremost emerging and important aspect people were following these days are fitness. The people are suffering from obesity which led to extreme medical disease. The body Fat Percentage (BFP) is the one of the most widely used method used to calculate the fitness level of the human. This study proposes the method to predict the BFA using the regression method. It also prescribes how people can maintain proper diet for being healthy and stay fit. Based on the prediction result, the web application provide people with the prescribed module like gym, proper diet, doctor consultation etc. The people can choose their respective module according to their fitness level. Also, the doctor consultant module is implemented using support vector machine (SVM) which classify the people according to the level of fitness and also the appropriate choice of doctor. The prediction result reveals the proposed model efficiency
Proposing a Load Balancing Algorithm For Cloud Computing Applications
Authors: Amal Murayki Alruwaili Bayan A. Al , Humayun Mamoona , Jhanjhi NZ 
Published: August 2021

Abstract
At the current time, one of the fields that are quickly developing in computing is clouding computing (CC). There are vital services needed by individuals as well as organizations that are offered in CC, namely IaaS, SaaS, and PaaS. Through the creation of the cloud, the development of the application has become easier, as well as providing services to end-users through virtualization over the internet. In CC, we have cloud service providers who deal with large computing structures completely defined on usage as they offer their services in a dependable mode. However; CC is facing the issue of load balancing, which impacts the performance and makes it weak if we overload the system. Developing an effective load balancing algorithm while utilizing CC efficiently is one of the providers’ ultimate goal. Virtualization and scalability are other dynamic techniques which entail developing VMs in CC. Data traffic and Web services provision is growing each day; thus, load balancing is a major challenge in the field of CC and is increasing the importance of task scheduling as well. At this point in time, evolvement in CC necessitates the greater need for infrastructure and resource development. The load balancing policy guarantees the efficient usage of the resource through the provision of services to the cloud subscribers. Load balancing also prioritizes subscribers by using an appropriate schedule. As a contribution in research, this paper proposes an algorithm for load balancing in CC. Proposed algorithm enhance the performance of Cloud Computing applications
Security and Privacy Issues in Cloud Computing
Authors: Alenizi Bayan A. , Humayun Mamoona , Jhanjhi NZ 
Published: August 2021

Abstract
The idea of Cloud Computing (CC) provides dynamically scalable services that are delivered over the Internet as a service. The key driver of the cloud is economic benefits, as it aims to minimize capital expenditure as well as operating expenditure. There are still some problems to be overcome in order for this to become reality. One of the important issue is security and privacy concerns, which has been addressed by many researchers but still the problem persist. Security assurance is an important driver for cloud adoption and for increasing cloud deployment. To provide a detailed overview of existing cloud security challenges and mitigation strategies, this paper provides a comprehensive survey of underlying cloud security and privacy issues and concerns along with countermeasures. Further; as a contribution in research, we have provided a framework to address the security and privacy concerns in CC. Proposed framework uses hybrid authentication mechanism for the security of CC. The study provides a deeper insight to the researchers and practitioners about CC and underlying security and privacy concerns along with countermeasures and a novel solution.
An Automated Hybrid Approach for Multimodal Tumor Segmentation
Authors: Sumathi R , Venkatesulu M. 
Published: August 2021

Abstract
For the past few years many people in the entire universe lost their lives due to cancer diseases like breast cancer, brain tumor, lung cancer and skin cancer etc. Many modalities like US, mammogram, CT are used to analyze the masses of cancer but its radiation effects the health for this reason MRI imaging is used for analyzing the anatomy behavior of tumors in terms of size of tumor, growth and location in detail. An automated hybrid approach with adaptive kernel fuzzy C Means with PSO is used to segment the tumor part in efficient manner. Using BRATS and RIDER MRI datasets are used for validation. Our proposed methods yields 97.1% segmentation accuracy and compared with various existing approaches like K Means and Adaptive K Means.