bluuebunny/crossref_metadata_2025 huggingface dataset:
Computational study of mechanical stability and phonon properties of MXenes Mo2ScC2T2 (T = O and F): 2D materials
Authors: Bolen E. , Deligoz E.
Published: August 2021

The structural, electronic, elastic, and phonon properties of pristine Mo2ScC2 and surface terminated Mo2ScC2T2 (T = O and F) were investigated by employing density functional theory calculations. Generalized gradient approximation was used to model exchange–correlation effects. The electronic band structure was calculated using both Perdew–Burke–Ernzerhof and Heyd–Scuseria–Ernzerhof functional and found that all considered materials are metallic. Elastic constants, Young's moduli, shear moduli, in-plane stiffnesses, and Poisson's ratios were tabulated and showed that pristine Mo2ScC2 and surface terminated Mo2ScC2T2 satisfy the mechanical stability criteria. Furthermore, Mo2ScC2O2 has been found to be a significant candidate for ultrasensitive sensors due to its ultrahigh Young's modulus. The absence of the imaginary line in phonon band structures confirms the dynamic stability of Mo2ScC2, Mo2ScC2O2, and Mo2ScC2F2. Furthermore, Debye temperatures, phonon group velocities, thermodynamic properties, and Grüneisen parameters were calculated for Mo2ScC2 and Mo2ScC2O2. Finally, it is understood that Mo2ScC2 and Mo2ScC2O2 are suitable candidates for applications that require low thermal conductivity.
A deep learning approach to the forward prediction and inverse design of plasmonic metasurface structural color
Authors: Roberts Nathan Bryn , Keshavarz Hedayati Mehdi
Published: August 2021

This report details a deep learning approach to the forward and inverse designs of plasmonic metasurface structural color. Here, optimized Deep Neural Network models are presented to enable the forward and inverse mapping between metamaterial structure and corresponding color. The forward model is capable of predicting color with >96% accuracy, with a 105 order of magnitude decrease in computational time when compared to finite-difference time-domain simulations used in conventional design workflows. An inverse model is trained using a tandem autoencoder, employing the pre-trained forward model. Here, the use of synthetic training data for self-learning is reported, which results in an ≈15% improvement in training accuracy. The tightly constrained inverse model allows for the instantaneous design of metasurfaces, given a desired color, with an accuracy of >86%, making it suitable for commercial use as well as the acceleration of photonics research.
Feedback control of protein aggregation
Authors: Dear Alexander J. , Michaels Thomas C. T. , Knowles Tuomas P. J. , Mahadevan L.
Published: August 2021

The self-assembly of peptides and proteins into amyloid fibrils plays a causative role in a wide range of increasingly common and currently incurable diseases. The molecular mechanisms underlying this process have recently been discovered, prompting the development of drugs that inhibit specific reaction steps as possible treatments for some of these disorders. A crucial part of treatment design is to determine how much drug to give and when to give it, informed by its efficacy and intrinsic toxicity. Since amyloid formation does not proceed at the same pace in different individuals, it is also important that treatment design is informed by local measurements of the extent of protein aggregation. Here, we use stochastic optimal control theory to determine treatment regimens for inhibitory drugs targeting several key reaction steps in protein aggregation, explicitly taking into account variability in the reaction kinetics. We demonstrate how these regimens may be updated “on the fly” as new measurements of the protein aggregate concentration become available, in principle, enabling treatments to be tailored to the individual. We find that treatment timing, duration, and drug dosage all depend strongly on the particular reaction step being targeted. Moreover, for some kinds of inhibitory drugs, the optimal regimen exhibits high sensitivity to stochastic fluctuations. Feedback controls tailored to the individual may therefore substantially increase the effectiveness of future treatments.
Phonon- and defect-limited electron and hole mobility of diamond and cubic boron nitride: A critical comparison
Authors: Sanders Nocona , Kioupakis Emmanouil
Published: August 2021

Diamond and cBN are two of the most promising ultra-wide bandgap semiconductors for applications in high-power high-frequency electronic devices. Despite extensive studies on carrier transport in these materials, there are large discrepancies in their reported carrier mobilities. In this work, we investigate the phonon- and dopant-limited electron and hole mobilities of cBN and diamond with atomistic first-principles calculations in order to understand their fundamental upper bounds to carrier transport. Our results show that although the phonon-limited electron mobilities are comparable between cBN and diamond, the hole mobility is significantly lower in cBN due to its heavier hole effective mass. Moreover, although lattice scattering dominates the mobility at low doping, neutral impurity scattering becomes the dominant scattering mechanism at higher dopant concentrations due to the high dopant ionization energies. Our analysis provides critical insights and reveals the intrinsic upper limits to the carrier mobilities of diamond and cBN as a function of doping and temperature for applications in high-power electronic devices.
Enhanced DC breakdown strength of epoxy nanocomposites at elevated temperature and its mechanisms
Authors: Li Zhen , Min Daomin , Niu Huan , Li Shijun , Zhang Yuanyuan , Huang Yin , Li Shengtao
Published: August 2021

Breakdown of epoxy composites is easy to be triggered as the temperature is elevated. In order to improve the DC breakdown strength of epoxy composites at elevated temperature and explore the DC breakdown mechanism, functional nano-titania (TiO2) particles were incorporated into the epoxy matrix with different filler loadings, molecular chain dynamic characteristics were analyzed by dielectric relaxation spectrum analysis, free volumes of epoxy nanocomposites were evaluated by thermal expansion dilatometer, and DC breakdown strengths of samples were tested at 413 K. Results indicate that DC breakdown strength first increases and then decreases with nanoparticle filler loadings, and a 10.89% improvement of DC breakdown strength is found compared to pristine epoxy resin. The breakdown strength of epoxy resin at elevated temperature is determined by the expansion properties of free volume in the interfacial region between the epoxy matrix and nanoparticles. When incorporating a small amount of nanoparticles, free volume is difficult to expand due to the strong interactions between molecular chains and nanoparticles, the fraction of free volume decreases, and long molecular chains of epoxy are hard to move, and thus DC breakdown strength increases. While further adding nanoparticles, interfacial regions of nanoparticles overlap and free volumes are likely to expand by thermal stimulation in the overlap region, which accelerate molecular chain dynamics and improve free volume fraction, and DC breakdown strength increases. It can be found that DC breakdown strength at an elevated temperature can be enhanced by tailoring free volume through incorporating proper content of functional nanoparticles.
A graphene edge-mediated quantum gate
Authors: Weinberg Phillip , Feiguin Adrian E.
Published: August 2021

We propose a quantum gate architecture that allows for the systematic control of the effective exchange interactions between magnetic impurities embedded in nanoscale graphene flakes connected by a gated bridge. The entanglement between the magnetic moment and the edge states of the fragments is used to electrostatically tune the exchange interaction from ferro to antiferromagnetic by merely changing the bridge's carrier density. By characterizing the effects of size and coupling parameters, we explore different operation regimes of this device by means of exact calculations with the density matrix renormalization group. We analyze the results utilizing a simplified model that accounts for the main many-body mechanisms. Finally, we discuss how to use arrays of these devices to build quantum simulators for quantum many-body Hamiltonians.
Accuracy of the distinguishable cluster approximation for triple excitations for open-shell molecules and excited states
Authors: Schraivogel Thomas , Kats Daniel
Published: August 2021

The distinguishable cluster approximation for triple excitations has been applied to calculate thermochemical properties and excited states involving closed-shell and open-shell species, such as small molecules, 3d transition metal atoms, ozone, and an iron–porphyrin model. Excitation energies have been computed using the ΔCC approach by directly optimizing the excited states. A fixed-reference technique has been introduced to target selected spin-states for open-shell molecular systems. The distinguishable cluster approximation consistently improves coupled cluster with singles doubles and triples results for absolute and relative energies. For excited states dominated by a single configuration state function, the fixed-reference approach combined with high-level coupled-cluster methods has a comparable accuracy to the corresponding equation-of-motion coupled-cluster methods with a negligible amount of spin contamination.
Data-driven many-body models enable a quantitative description of chloride hydration from clusters to bulk
Authors: Caruso Alessandro , Paesani Francesco
Published: August 2021

We present a new data-driven potential energy function (PEF) describing chloride–water interactions, which is developed within the many-body-energy (MB-nrg) theoretical framework. Besides quantitatively reproducing low-order many-body energy contributions, the new MB-nrg PEF is able to correctly predict the interaction energies of small chloride–water clusters calculated at the coupled cluster level of theory. Importantly, classical and quantum molecular dynamics simulations of a single chloride ion in water demonstrate that the new MB-nrg PEF predicts x-ray spectra in close agreement with the experimental results. Comparisons with an popular empirical model and a polarizable PEF emphasize the importance of an accurate representation of short-range many-body effect while demonstrating that pairwise additive representations of chloride–water and water–water interactions are inadequate for correctly representing the hydration structure of chloride in both gas-phase clusters and solution. We believe that the analyses presented in this study provide additional evidence for the accuracy and predictive ability of the MB-nrg PEFs, which can then enable more realistic simulations of ionic aqueous systems in different environments.
Thermal stability of epitaxial α-Ga2O3 and (Al,Ga)2O3 layers on m-plane sapphire
Authors: McCandless J. P. , Chang C. S. , Nomoto K. , Casamento J. , Protasenko V. , Vogt P. , Rowe D. , Gann K. , Ho S. T. , Li W. , Jinno R. , Cho Y. , Green A. J. , Chabak K. D. , Schlom D. G. , Thompson M. O. , Muller D. A. , Xing H. G. , Jena D.
Published: August 2021

Here, we have explored the thermal stability of α-(Al,Ga)2O3 grown by the molecular-beam epitaxy on m-plane sapphire under high-temperature annealing conditions for various Al compositions (i.e., 0%, 46%, and 100%). Though uncapped α-Ga2O3 undergoes a structural phase transition to the thermodynamically stable β-phase at high temperatures, we find that an aluminum oxide cap grown by atomic layer deposition preserves the α-phase. Unlike uncapped α-Ga2O3, uncapped α-(Al,Ga)2O3 at 46% and 100% Al content remain stable at high temperatures. We quantify the evolution of the structural properties of α-Ga2O3, α-(Al,Ga)2O3, and α-Al2O3 and the energy bandgap of α-Ga2O3 up to 900 °C. Throughout the anneals, the α-Ga2O3 capped with aluminum oxide retains its high crystal quality, with no substantial roughening.
Understanding the co-evolutionary relationships between Indigenous cultures and non-native species can inform more effective approaches to conservation: the example of pigs (pua’a;
Authors: Luat-Hū‘eu Kūpa‘a K. , Winter Kawika B. , Vaughan Mehana B. , Barca Nicolai , Price Melissa R.
Published: August 2021

Indigenous resource management (IRM) is dynamic and ever evolving, in part because it is based on co-evolutionary relationships between Indigenous cultures and the biodiversity around them. Forms of conservation imposed on Indigenous people and places by settler-colonialism tend to idealise pre-human and human-excluded environments, leading to conflicts between settler-coloniser conservationists and Indigenous communities detrimental to conservation goals. Conservation efforts that align with IRM and acknowledge the co-evolutionary relationships at the foundation of Indigenous culture can lead to more effective conservation efforts. In Hawai‘i, the evolving relationship between Kānaka (Hawaiians) and pua‘a (pigs; Sus scrofa) has been the flash point of conflicts between settler-coloniser conservationists and Hawaiian communities. This paper examines the co-evolving relationships between Hawaiians and pigs in an effort to better balance the conservation efforts aimed at controlling invasive species with the State of Hawai‘i’s obligation to support Indigenous practices and public hunting. We conducted this research by investigating archival Hawaiian language resources, which allowed us to resurrect knowledge lost to time and pinpoint key historical changes over the past 250 years. Our results elucidate this co-evolutionary relationship that shifted from an animal-husbandry relationship to a hunter–prey relationship in the first half of the 19th century. This change in the trajectory of the co-evolutionary relationship was a result of various shifts throughout Hawaiian socio-ecological systems, and therefore necessitates adaptive governance relating to management of and access to pigs. We conclude that Indigenous perspectives offer opportunities to transform conservation biology through multi-objective approaches that address both hunting and conservation goals.
Plant uptake of CO2outpaces losses from permafrost and plant respiration on the Tibetan Plateau
Authors: Wei Da , Qi Yahui , Ma Yaoming , Wang Xufeng , Ma Weiqiang , Gao Tanguang , Huang Lin , Zhao Hui , Zhang Jianxin , Wang Xiaodan
Published: August 2021

Significance

Cold regions contain vast stores of permafrost carbon. Rapid warming will cause permafrost to thaw and plant respiration to accelerate, with a resultant loss of CO2, but could also increase the fixation of CO2by plants. A network of 32 eddy covariance sites on the Tibetan Plateau, which has the largest store of alpine permafrost carbon on Earth, shows that this region functions as a net CO2sink. Our sensitivity analyses, experiments, and model simulations consistently showed that the fixation of CO2by plants outpaces the loss of CO2from permafrost and accelerates plant respiration. This indicates a plant-dominated CO2balance on the Tibetan Plateau, which could provide a negative feedback to climate warming.
Functional cross-talk between phosphorylation and disease-causing mutations in the cardiac sodium channel Na
v
1.5
Authors: Galleano Iacopo , Harms Hendrik , Choudhury Koushik , Khoo Keith , Delemotte Lucie , Pless Stephan Alexander
Published: August 2021

Significance

The cardiac sodium channel (Na
v
1.5) is crucial for generating a regular heartbeat. It is thus not surprising that Na
v
1.5 mutations have been linked to life-threatening arrhythmias. Interestingly, Na
v
1.5 activity can also be altered by posttranslational modifications, such as tyrosine phosphorylation. Our combination of protein engineering and molecular modeling has revealed that the detrimental effect of a long QT3 patient mutation is only exposed when a proximal tyrosine is phosphorylated. This suggests a dynamic cross-talk between the genetic mutation and a neighboring phosphorylation, a phenomenon that could be important in other classes of proteins. Additionally, we show that phosphorylation can affect the channel’s sensitivity toward clinically relevant drugs, a finding that may prove important when devising patient-specific treatment plans.
Cloud cooling effects of afforestation and reforestation at midlatitudes
Authors: Cerasoli Sara , Yin Jun , Porporato Amilcare
Published: August 2021

Significance

Afforestation and reforestation are among the most appetible natural climate solutions, but their actual benefits are still unclear at midlatitudes (some studies have even claimed to be detrimental). We combine satellite data and atmospheric boundary-layer models to show that the wider occurrence of clouds over forests, compared to other types of land cover, implies a clear benefit of afforestation and reforestation at midlatitudes, which was previously unaccounted for.
Moonrise timing is key for synchronized spawning in coral
Dipsastraea speciosa
Authors: Lin Che-Hung , Takahashi Shunichi , Mulla Aziz J. , Nozawa Yoko
Published: August 2021

Significance

Mass coral spawning is a perplexing annual event that occurs over a few nights following a full moon. This paper demonstrates that the period of darkness between sunset and moonrise that occurs after the full moon is a trigger for spawning in coral species
Dipsastraea speciosa
. As this species shares similar spawning patterns with many other coral taxa, we expect this model for spawning to be applicable to other coral species. Our discovery provides insights into how coral spawning is synchronized via the lunar cycle.
Layering transitions and metastable structures of cholesteric liquid crystals in cylindrical confinement
Authors: Eun Jonghee , Pollard Joseph , Kim Sung-Jo , Machon Thomas , Jeong Joonwoo
Published: August 2021

Significance

A material’s topology is more than its shape. It governs the material’s physical properties, such as how it interacts with acoustic waves and transports charge carriers. Utilizing liquid crystals (LCs) and their defects, researchers have pursued understanding topological aspects of condensed matter. Our work discusses the topological properties of cholesteric LCs that twist spontaneously. The traditional topological descriptions of LC do not extend to a complete characterization of cholesterics, and yet they often exhibit behavior with a strong topological and geometric character. We show that a more subtle chiral topology, protected by nonvanishing twist, can be used to define new topological numbers and understand these systems’ behavior, including the metastability, layering transitions, and soliton structures that traditional invariants cannot describe.
High-throughput dissection of the thermodynamic and conformational properties of a ubiquitous class of RNA tertiary contact motifs
Authors: Bonilla Steve L. , Denny Sarah K. , Shin John H. , Alvarez-Buylla Aurora , Greenleaf William J. , Herschlag Daniel
Published: August 2021

Significance

We would like to have a predictive understanding of how RNAs form and interconvert between intricate three-dimensional shapes that are essential to function. Despite great structural diversity, RNAs are built from a limited set of structural motifs so that a predictive model may arise from the in-depth understanding of these building blocks. We used a quantitative, high-throughput approach to investigate the thermodynamic and conformational properties of the tetraloop/tetraloop receptor (TL/TLR), a ubiquitous RNA tertiary contact motif. Our investigation of 1,493 TLRs and two TLs revealed classes of TL/TLR thermodynamic behavior. One class displayed conserved conformation across sequences and appears to be thermodynamically selected in biology. Other classes are conformationally diverse and may be selected for flexibility instead of stability.
Power and efficiency scaling of Er: ZBLAN fiber laser
Authors: Newburgh G A , Dubinskii M
Published: August 2021

We report on a 976 nm pumped erbium doped fluoride (Er:ZBLAN) fiber laser emitting at 2.8 µm with nearly 70 W of output power in a quasi-continuous wave (Q-CW) regime with the slope efficiency versus launched pump power of ∼20%. It is believed to be the highest Q-CW power ever demonstrated from an Er:ZBLAN fiber laser . We also present the results of a study demonstrating that the Er:ZBLAN fiber laser slope efficiency versus absorbed pump power increases monotonically with the pump wavelength from 19% to 29% over the wavelength range of 960–985 nm. The achieved slope efficiency of 29% is, to the best of our knowledge, the highest efficiency ever reported for an Er:ZBLAN fiber non-cascaded laser.
Rainfall Prediction In Nagapattinam Using Fuzzy Logic
Authors: Santha S. , Brindha Devi M. K.
Published: August 2021

Rainfall plays a vital role in human life rather than other weather events. As the variations are unpredictable, it difficult to predict rainfall over a region, so that rainfall forecasting carried out by the metrological department has been most crucial and demanding one. Fuzzy logic models have been proposed to perform forecasting, and its prime operations are fuzzification and defuzzification. These models are simple to employ but offer better results than statistical models. The present study explores the strength of forecasting models based on fuzzy logic rules. Furthermore, predicted results are compared with the actual rainfall data.
Thermodynamics of Regular Black Holes Inspired by Noncommutative geometry Journal of Physics: Conference Series
Authors: Singh B K , Singh R P
Published: August 2021

We present a new, exact, solution of regular Schwarzschild black hole by introducing an anisotropic perfect fluid inspired by noncommutative geometry. The obtained solution is interpolated between two quantities which are the de Sitter space-time at little distance and the regular Schwarzschild geometry at extensive extent. The consequence of noncommutative geometry is that it alters the thermodynamical characteristics of the black hole. Hawking temperature is calculated and its graphical study affirms striking features with reference to the alteration of the Hawkins temperature with the petty radius of the black hole.
Radio Mean Labeling of Some Splitting Graphs
Authors: Palani K. , Sabarina Subi S.S. , Maheswari V.
Published: August 2021

Let G = (V, E) be a simple graph with p vertices and q edges. For a connected graph G of diameter d, a radio mean labeling is a one to one mapping f from V(G) to N satisfying the condition

d
(
u
v
)
+
[

f
(
u
)
+
f
(
v
)

2

]
≥
1
+
d
i
a
m
(
G
)

for every μ, ν ∈ V (G). The span of a labeling f is the maximum integer that f maps to a vertex of G. The radio mean number of G, r
m
n(G) is the lowest span taken over all radio mean labelings of the graph G. In this paper, we analyze some splitting graphs for radio mean labeling.
q Sets in μNTopological Spaces
Authors: Hari Siva Annam G. , Raksha Ben N.
Published: August 2021

In this article, q sets in μ

N
TS are to be introduced and their basic properties are to be discussed. Some operators of the q Sets in μ

N
Topological Spaces such as μ

N

q Frontier, μ

N

q Exterior, μ

N

q Border are to be introduced and their characters are to be contemplated.
Effect of anisotropy pores on thermohaline convective instability in micropolar ferrofluid
Authors: Ratchagar Nirmala P , Seyalmurugan S.
Published: August 2021

This work deals with the thermohaline convective instability on horizontal micropolar ferrofluid layer in anisotropic porous effect subjected to a transverse uniform magnetic field. The linear stability analysis is used to omit the non-linear terms on governing equations, normal mode analysis is taken to the study and free boundary conditions are applied. The critical magnetic thermal Rayleigh number N

SC
is calculated analytically for sufficient large values of M
1 The principle of exchange of stabilities is satisfied for micropolar ferrofluid in an anisotropic porous medium without N
1, N
’
3, N
’
5 and τ. The sufficient conditions for the non-existent of overstability are also examined. Moreover, in this work, we tried to investigate the anisotropy effect on porous medium on the system. The parameters N
1and N
5
’ dominate the system and the large porous medium is taken into account.
Identity Protection during the use of Internet
Authors: Agrawal Navin Kumar
Published: August 2021

In today’s era, security become important concern. As we are moving towards the digitalization data protection is important issue. To make the data as well system safer we must follow some simple but important steps. These steps works just as safe guard. The concern regarding use of internet become more when we are making digital payment or sharing some useful information. More awareness is also required in case of non-technical person whenever use the internet. In this era technology is reachable to every person. We also execute many financial activities through our mobile / Computer. So many observers are there who are regularly keeping any eye on our activities and never miss our single loophole. Some simple Dos and Don’ts can make the difference and create awareness in the society.
Scientific Exploration for Density Estimation and Crowd Counting of Crowded Scene
Authors: Singh Juginder Pal , Kumar Manoj , Arya Ayush , Baldev  , Badmera Bharat
Published: August 2021

Crowd density estimation model is a typical concept which compute the counting of the people in the crowded image. There are many of research papers are written in this area to solve the different kind of real world problems. This paper shows the state of art and different types of framework proposed on density estimation and crowd counting techniques. The population of our country increases year by year, so the purpose of this paper is to explore different techniques available for crowd density estimation. There are lot of applications existing based on this technique like retail system, surveillance system etc., where the foot traffic is very vital in retail system to organize merchandise in aisles, optimize store layout, understand peak times and potentially even protect against theft. In surveillance system, this is mainly used for security purpose. In this, the researcher captures the crowd using the camera and the technique of counting the crowd which is divided into two parts unsupervised and supervised learning. These techniques are further divided into parts which is shown in the paper. The survey presents the different types of datasets of the crowded image which is useful for simulating the people counting in the crowded scenes. In detection based approach, this technique counts the people with the help of face detection technique. In this researchers face the problems of count the people in crowded scenes and also face the problem in surveillance applications. The cluster based approach data works well on the scattered crowd. This method needs image frames; it does not work on still images. In regression approach the model trained is subject to the point of view map. In the model were to be used in a different scene of a changed perspective map, it will have many inaccuracies in its result. The current research work intends to provide a general idea on crowd density estimation and counting approaches employed in visual surveillance in the perception of the study of computer vision. The analysis of review categorizes and frameworks quite a lot approximation related to crowd density and calculating approaches which are beneficial in providing the analysis related to the crowd places of interest.
Symmetric bi-interior ideals of Symmetric Semigroups
Authors: Punitha Tharani A. , Uma Maheswari V.
Published: August 2021

In this paper, as a further generalization of ideals, we introduce the notion of symmetric bi-interior ideal as a generalization of symmetric quasi ideal, symmetric bi-ideal and symmetric interior ideal of symmetric semigroup and study the properties of symmetric bi-interior ideals of symmetric semigroup and simple symmetric semigroup.
Product-Set Labeling of Some Zero-Divisor Graphs and its Line Graph
Authors: Rajeswari R. , Aruna Sakthi K. , Meenakumari N.
Published: August 2021

Let G = (V, E) be a graph with p vertices and q edges. A graph has product-set labeling if there exist an injective function f: V(G) → P (N) such that the induced edge labeling function f
∗: V(G) → P(N) is defined as f
∗ (μ
ν) = f (μ) ∗ f (ν) ∀μ, ν ∈ E(G).f (μ) ∗ f (ν) = {ab: a ∈ f (μ), b ∈ f (ν) }. In this paper we investigate how product-set labeling works for the zero-divisor graph and line graph of zero-divisor graphs.
A Pre-Trained Vs Fine-Tuning Methodology in Transfer Learning
Authors: Gupta Neeraj
Published: August 2021

Transfer learning from a pre-trained and fine-tuning methodology has been utilized for the image classification. In this paper, we classify images of cats and dogs. It is much faster and easier than training from scratch using a pre-trained network with transfer learning. This saved network was trained previously on a huge dataset and known as a pre-trained model. The pre-trained model can be used in two ways, either it is used in the same way or it utilizes transfer learning approach to adapt this pre-trained network to a specific goal. Since the idea for image classification using transfer learning is that it is trained on the general and large dataset and further this model will be efficiently worked on the visual perception task of the real world. In fine-tuning, instead of tuning with the weights of generic feature map of based model, it is tuned accordingly to the dataset. In this paper, we take benefit of these learned feature maps deprived of having to start from scratch by utilizing a deep learning technique on a huge dataset and analyze the various methodology. The experimental results show that fine-tuning methodology outperformed over the pre-trained with feature extraction on accuracy.
Some New Wiener Index of Power 3 Mean Graphs
Authors: Sarasree  , Sandhya S.S.
Published: August 2021

The Wiener index W(G) of a connected graph G is the sum of distances of all pairs of vertices of G.
Sign Pairity Difference Cordial Labeling of Digraphs
Authors: Palani K. , Nivetha S. , Nagarajan A.
Published: August 2021

Let D = (V, A)be a digraph. An injective function f: V(D) → {1,2, …. p} is said to be a sign pairity difference cordial labeling if the induced arc labeling f
∗: A(D) → {0,1} defined byf
∗ ((μ, ν)) =

{

1

iff
(
u
)
−
f
(
v
)
>
0

0

elsewhere

satisfiesthe condition that |e

f
0-e

f
(1)| ≤ 1 where, e

f
(0) is the number of arcs with label 0 and e

f
(1) is the number of arcs with label 1. In this paper, we analyze the existence of sign pairity difference cordial labeling in the digraphs Directed Path

(

P
n

→

)

(
A

P

n

→

)

, Directed Cycle

(

C

n

→

)

, Outstar

(
o

K

1
,
n

→

)

, Inwheel

(
i

W

n

→

)

and Downcomb

(
Down

P

n

⊙

K

1

→

)

defined by K.Palani et.al.
Neighborhood Prime Labeling in Some Product And Power Digraphs
Authors: Palani K , Shunmugapriya A , Meenakumari N
Published: August 2021

Let D(p, q) be a digraph. A function f: V → {1, 2, …, n} is said to a neighborhood prime labeling of D if it is both in and out degree neighborhood prime labeling. In this paper, we introduce the concept of neighborhood prime labeling in digraphs and also investigate the existence of neighborhood prime labeling in some digraphs.
Minimal Bi-Ideal of Г-Near Subtraction Semigroups
Authors: Meenakshi P. , Meenakumari N.
Published: August 2021

In analogy with the concept of bi-ideal of Γ-near subtraction semigroups, we introduce the notion of minimal bi-deal of Γ-near subtraction semigroups. In this paper, we obtain a necessary and sufficient condition for a zero-symmetric Γ-near subtraction semigroup to be a Γ-near – field. Further we have proved some properties of minimal bi-deal of Γ-near subtraction semigroupsand its characterization theorems.
Sinusoidal Fuzzy Soft Set and its Application in Mcdm
Authors: Karthika R. , Sahaya Sudha A.
Published: August 2021

This paper deals with the study of extension of classical soft sets into a Sinusoidal fuzzy soft set based on Sinusoidal fuzzy number and the basic operations on a sinusoidal fuzzy soft set are defined. Then a new algorithmic approach for Multi Criteria decision making based on Sinusoidal fuzzy soft set has been furnished using the ranking techniques, finally it has been analyzed in detail and illustrated with a numerical example.
A Ophthalmology Study on Eye Glaucoma and Retina Applied in AI and Deep Learning Techniques
Authors: Vaishnavi S. , Deepa R. , Nanda kumar P.
Published: August 2021

Artificial intelligence based on deep learning has huge global interest in current times. Deep Learning has been widely affiliated in image and speech recognition and also natural language processing, but it is beginning of healthcare. In ophthalmology, Deep learning has applied in photographs fundus, optical coherence tomography and vision sectors. Accomplishes safety in classifying the detection performance on diabetic retinopathy, age-related macular degeneration. Deep learning may be used in concurrence with telemedicine as possible to diagnose and monitor major eye diseases for patients in primary care. There are also prospective challenges with Deep learning application in ophthalmology, including clinical and technical challenges, explain ability of the algorithm results, medico legal issues, and physician and patient acceptance of the AI “black-box” algorithms. Deep learning could probably revolutionize how ophthalmology is practiced in the future. Hence this reviewprovides a Deep Learning systems described for ophthalmic conditions, and fundamental functions.
Selection Of Best Social Media Using Group Decision With Picture Fuzzy Graphs
Authors: Priyanka R. , Preethi Ceon Y.
Published: August 2021

In this dissertation, we study the picture fuzzy graph (PFG) in the decision-making problem based on picture fuzzy relation and some types of picture fuzzy graphs such as complete picture fuzzy graphs, strong picture fuzzy graphs, complement picture fuzzy graphs are described and also study the Pythagorean fuzzy graph in decision making environment. Further, we present an application of Pythagorean Fuzzy Graphs and Picture Fuzzy Graphs in decision making environment.
Stability Enhancement of DFIG Based Wind Turbine Under Three Phase Fault with STATCOM
Authors: Lakshmi Priya J , Jaya Christa S.T
Published: August 2021

The main aim of this paper is to improve the transient stability of Doubly Fed Induction Generator (DFIG) based wind turbine using Static Synchronous Compensator (STATCOM). In this paper, a multi-objective problem is formulated to minimize voltage deviations at the point of common coupling (PCC) and mitigate low frequency oscillations after clearing the faults. Genetic algorithm based STATCOM controller is used to enhance the stability of a grid-connected wind farm composed of a variable-speed wind turbine generator system (WTGS). The transient performance of the proposed control scheme is analyzed with a three-phase fault. The Simulations are carried out in MATLAB/Simulink environment.
Experimental Study of Adsorption Efficiency of Methylene Blue Dye by Using Banana Leaf Biochar as an Adsorbent
Authors: Kumar Umesh , Vibhute Bhalchandra , Parikh Sachin
Published: August 2021

Removal of dye from wastewater has been a matter of concern, both as health point of view and in aesthetic sense. Colour removal from dye wastewater has been given much attention, not only because of its toxicity and water problem, but also mainly due to its visibility problem. There have been various promising techniques for the removal of dye from effluent. However, the adsorption effectiveness for dye removal from waste water has made it an ideal alternative to other expensive treatment methods. Agricultural wastes are inexpensive, biodegradable. Agricultural wastes are excellent sources for the production of adsorbent, which can be used as alternate, low cost treatment systems for the dye generated during the textile processing. Biochar was prepared by activating banana waste and used as an effective bio-sorbent to remove methylene blue (MB) dye from aqueous solution. Batch adsorption experiments are carried out using banana leaves as adsorbent. The adsorption study was performed by varying the adsorption parameter such as pH, adsorbent dose, contact time, temperature, agitation speed, and adsorbent particle size.
Treatment of Textile Wastewater Using Sodium Alginate Beads
Authors: Sharmila A , Mardera Madhumathi S , Soundarya H P , Shreyas M
Published: August 2021

Textile is one of the important industry. Conversion of fiber to fabric takes place which involves many process and synthetic chemicals. The fabric is then dyed or printed, fabricated into clothes. The extraction of dyes from the wastewater industry becomes a worldwide issue. Water contamination due to industrial discharge is the big threat to the environment. Hence an attempt was made in this study to treat textile wastewater using natural anionic polysaccharide sodium alginate as adsorbing agent. The optimum condition was obtained by varying dosage, pH, flow rate and depth of beads for varying contact time. For optimum conditions, the textile wastewater was analyzed. Results showed 32%, 56% and 75% removal of sulphates, alkalinity and nitrates respectively. Hence sodium alginate proved efficient in treatment of textile wastewater.
A Glimpse on Possible Detection Tools and Vaccines for Mitigation and Management of COVID-19
Authors: Rajasulochana P. , FahiraTasneem S. , Kapoor Ashish , Chandan Monit Kumar
Published: August 2021

The world is made up of all kinds of living organisms which also includes microorganisms like bacteria, virus and fungus. One of them coronavirus was rare one but in 2019, a new strain called SARS-CoV-2 was discovered in Wuhan, China which was quite similar to SARS-CoV Virus. Days went and the world was in pandemic. The virus, subsequently named COVID-19 virus was affecting all over the world and lives were in peril. People got aware of its symptoms and precautions declared by World Health Organization (WHO). COVID-19 was mainly diagnosed by RT-PCR and antigen tests. After crossing many hurdles, scientists developed vaccines and some of them like Comirnaty, mRNA-1273 and AstraZeneca vaccines got approved by FDA and it was developed by many premier institutes. So far, at least 5.9 million people in United States have received their first dosage and other vaccines are under clinical trials. This review provides a glimpse on various aspects related to COVID-19 including diagnostic tools and vaccination
Comparative Analysis Of In Vitro Antidiabetic Activity Of Wild And Micropropagated Solanum Trilobatum L
Authors: Rajasulochana P. , Battineni Abhigna , Fahira Tasneem S.
Published: August 2021

Diabetes is metabolic disorder which is characterized by an increase in glucose level due to a relative deficiency of insulin. Recent decades have experienced a very high prevalence of diabetes mellitus. One way of treating diabetes is to reduce gastrointestinal glucose production by inhibiting carbohydrate digesting enzymes called pancreatic alpha-amylase and intestinal alpha-glucosidase. The present study aims to compare the efficacy of wild and micropropagated Solanum trilobatum for its in vitro antidiabetic property. Solanum trilobatum grown in vitro under aseptic conditions was compared with wild-grown, and the results have revealed that both wild and micropropagated plants have effective dose-dependent inhibition of alpha-amylase and alpha-glucosidase enzyme. For both alpha-amylase and alpha-glucosidase assay micropropagated plant has shown lesser efficacy at all concentrations when compared to that of wild grown. still micropropagated showed effective inhibition with nearly 90% similarity.
A Mathematical Modelling of A Multi-Physics Ensemble Approach for Exploring the Sensitivity of Climate
Authors: Gupta Raj Kumar , MagnoVentayen Randy Joy , Saravanakumar R , Salahuddin Ghazal , Nomani M.Z.M.
Published: August 2021

The amount of global surface warming that will effectively respond to twice of atmospheric CO2 concentrations compared with pre-industrial levels is referred to as climate sensitivity. The aim to explore the sensitivity of climate by using the mathematical model of the multi-physics ensemble approach. It’s considered as a multi-physics MM5 ensemble of 30 years hindcast simulations run through a complicated and climatically varied area. In this study, eight multi-physics ensembles (MPEs) models were used, MIROC5 physics systems were replaced with MIROC3 physics systems. The analysis is based on a seasonal time scale with an emphasis on average temperature and precipitation values as well as interannual variability. Multi-parameter MPE was made a set ensemble of perturbed-physics in which the parameter value for individual MPE model is swept. The previously evaluated MPE approach can be better understand and improve in the simulation of the multi-physics climate by using Bayesian inference. Bayesian inference allows actions often associated with a post-model flexible project to be incorporated into the model development process. As a result, an ensemble of model configurations has been created, which allows for a more thorough assessment of the remaining uncertainties. The value of model physics is shown by demonstrating that the dispersion between experiments is comparable.
Battery Management System for E-Vehicle using Kalman Filter
Authors: Balachander K , Amudha A , Naveen KT
Published: August 2021

For safe and proper battery management system the main aspect is to do a optimization of SOC which is State-of-Charge estimation. This paper gives you the maximum achievement of BMS with the electric vehicle Lithium ion Battery. Kalman filter design is implemented in this in order to reduce the mechanical noise and further voltage and current ripples where the man aim of this research work using Kalman is that it must have some proper sequence like a proper electronics and electrical model to get rid of the noises and ripples, thus the models current state and its system design is verified where it can apply to all sorts of problems and can apply to all such current manufacturers. From this point of view, we implemented a design which matches the output source of Kalman filter design and takes the less time for giving the accurate output. Hence the simulation with the Kalman filter design and its respective needed electronics components are therefore simulated and programmed by the MATLAB Simulink.
Big Data Technology for Developing Learning Resources
Authors: Sharma Kuldeep , Mahesh T R , Bhuvana J
Published: August 2021

Endeavours for adopting network data greatly from social media allude to give the specific methods in extracting value from data space, for example, conversion, transaction, message and others, where structured information sources originate from big business assets information and unstructured information sources originate from video and audio. It very well may be accomplished to extend the way toward extracting value from social network for designing the information sources to satisfy the association objective. This paper means to uncover the method for approach of big data in extracting information esteem from information complexity including velocity and variety into volume. This investigation was led employing contents analysis by looking into certain literary works in chapters, books and peer-audited journals and procedures in creating prototype employing data analytics related from users, time analytics and topic. The discoveries uncover that big data rising technology with analytics procedure gives specific favourable circumstances to change the trend of data fitted into innovative atmosphere of OLR i.e. online learning resources to upgrade in building up learning resources. Both model and prototype information extraction value can be upgraded to encourage the learning atmosphere in supporting the implementations with convenience and ease. This investigation is relied upon to add to improve the outcomes and learning atmosphere with achievement and performance by upgrading the learning process development of students to give online resources in the higher education setting.
Convolutional Neural Network Using for Multi-Sensor 3D Object Detection
Authors: Sudhansu Gadug , Mohamed Zabeeulla A N , Nachappa M N
Published: August 2021

The purpose of this article is to detect 3D objects inside the independent vehicle with great accuracy. The method proposed a Multi-View 3D System (MV3D) framework which encodes the sparse 3d-point cloud with a compact multi-view image, using LIDAR satellite image and RGB pictures as inputs, and predicts 3D boundary boxes. The network comprises two sub-networks: one for creating 3D artifacts and one for multi-visual fusion functionality. Propose an autonomous 3D object tracking approach to manipulate sparse and dense knowledge about romanticizing and geometry in stereo images. The Stereo R-CNN strategy applies Faster R-CNNs to stereo inputs such that objects are simultaneously identified and linked in conservative and liberal images. Such charts were then combined and fed into a 3D proposal generator to generate accurate 3D proposals for vehicles. In the second step, the refining network extended the features of the proposal regions further and carried through the classification, regression of a 3D package box, and guidance estimates, to predict vehicle location and heading in 3D area and add additional branches after the stereo region Proposal Network (RPN).
Evaluation of Filtering Techniques in Channel Estimation for MIMO - OFDM Systems
Authors: Karthickmanoj R , Padmapriya J , Sasilatha T. , Aagash G
Published: August 2021

Noise in a signal is an unwanted Distribution occurred which disrupts the original signalduring transmission, Depuration or conversion. Also noise may be random, with an unspecific mean or white noise with an independently interleaved distribution, zero mean by the processing algorithms. Performance degradation of Orthogonal Frequency Division Multiplexing system with Multiple Input and Multiple Output would have ensued due to the noise distribution in an original signal, so it is inevitable to reduce the unwanted noise distribution in an original signal to recover the original information which is sent at the transmitter. Various filtering techniques in channel estimation have been proposed in this literature to denoise a signal in Multiple Input Multiple Output – OFDM system effectively. The main intent of this survey is to provide the better insight of various denoising techniques in channel estimation to reduce unknown noise distributions in a signal with the tradeoff complexity in MIMO – OFDM systems.
Neural Network Based Weeding Robot For Crop And Weed Discrimination
Authors: Florance Mary M , Yogaraman D
Published: August 2021

Weeding is the most difficult process in the field of agriculture, which may consume more manpower, time and money. This paper is designed for detecting out weeds in-between crops and drilling them using blade attached at the end of robotic. Though this robot works autonomously, this does not require more manpower. It offers reduced working time and requires less installation cost. Insufficient labour, improper weeding, pandemic situation like Covid19 are some problems which can be avoided by this robot. This form of weeding is eco-friendly and does not affect the organic nature of the crop. It uses Convolution neural network(CNN) for detecting the image of weed from the crop.