bluuebunny/crossref_metadata_2025 huggingface dataset:
Tuning of regimes during two-phase flow through a cross-junction
Authors: Boruah Manash Protim , Sarker Anik , Randive Pitambar R. , Pati Sukumar , Sahu Kirti Chandra
Published: December 2021

We investigate the dynamics of two immiscible fluids in a cross-junction via three-dimensional numerical simulations using the volume of fluid approach to track the dispersed phase's evolution. Different regimes, namely the dripping, squeezing, and jetting dynamics, have been observed for different dimensionless parameters, and we unveil a transition in regimes due to the concomitant interplay of capillarity, viscosity, and wettability. Our results reveal that hydrophobic channel surfaces favor a transition from squeezing to dripping behavior at a lower value of the capillary number. Moreover, higher viscosity ratios advance the process of squeezing, necking, and breakage on hydrophobic surfaces. A wettability–capillarity regime map is also presented that will have significant implications regarding the choice of substrate wettability, fluid properties, and flow rate in droplet dispensing devices.
Machine learning accelerated turbulence modeling of transient flashing jets
Authors: Schmidt David , Maulik Romit , Lyras Konstantinos
Published: December 2021

Modeling the sudden depressurization of superheated liquids through nozzles is a challenge because the pressure drop causes rapid flash boiling of the liquid. The resulting jet usually demonstrates a wide range of structures, including ligaments and droplets, due to both mechanical and thermodynamic effects. As the simulation comprises increasingly numerous phenomena, the computational cost begins to increase. One way to moderate the additional cost is to use machine learning surrogacy for specific elements of the calculation. This study presents a machine learning-assisted computational fluid dynamics approach for simulating the atomization of flashing liquids accounting for distinct stages, from primary atomization to secondary breakup to small droplets using the Σ−Y model coupled with the homogeneous relaxation model. Notably, the models for thermodynamic non-equilibrium (HRM) and Σ−Y are coupled, for the first time, with a deep neural network that simulates the turbulence quantities, which are then used in the prediction of superheated liquid jet atomization. The data-driven component of this method is used for turbulence modeling, avoiding the solution of the two-equation turbulence model typically used for Reynolds-averaged Navier–Stokes simulations for these problems. Both the accuracy and speed of the hybrid approach are evaluated, demonstrating adequate accuracy and at least 25% faster computational fluid dynamics simulations than the traditional approach. This acceleration suggests that perhaps additional components of the calculation could be replaced for even further benefit.
Numerical validation of the dusty-gas model for binary diffusion in low aspect ratio capillaries
Authors: Vaartstra Geoffrey , Lu Zhengmao , Grossman Jeffrey C. , Wang Evelyn N.
Published: December 2021

The dusty-gas model is an empirical formulation commonly used to describe gas flows in porous media. While experiments have validated the model for pores with high aspect ratios, a validation for near-unity aspect ratios is lacking. We used direct simulation Monte Carlo to evaluate the accuracy of the dusty-gas model for binary diffusion in low aspect ratio capillaries. Our results suggest that the dusty-gas model deviates from the Monte Carlo simulations by less than 3% for aspect ratios ≥ 1, thus supporting its suitability as a phenomenological model for transport in nanoporous structures in various applications such as heat transfer, catalysis, and water purification.
Geometry and boundary condition adaptive data-driven model of fluid flow based on deep convolutional neural networks
Authors: Peng Jiang-Zhou , Aubry Nadine , Zhu Shiquan , Chen Zhihua , Wu Wei-Tao
Published: December 2021

We develop a deep neural network-based reduced-order model (ROM) for rapid prediction of the steady-state velocity field with arbitrary geometry and various boundary conditions. The input matrix of the network is composed of the nearest wall signed distance function (NWSDF), which contains more physical information than the signed distance function (SDF) and binary map; the boundary conditions are represented by specifically designed values and fused with NWSDF. The network architecture comprises convolutional and transpose-convolutional layers, and convolutional layers are employed to encode and extract the physical information from NWSDF. The highly encoded information is decoded by transpose-convolutional layers to estimate the velocity fields. Furthermore, we introduce a pooling layer to innovatively emphasize/preserve information of boundary conditions, which are gradually flooded by other features during the convolutional operation. The network model is trained using several simple geometries and tested with more complex cases. The proposed network model shows excellent adaptability to arbitrary complex geometry and variable boundary conditions. The average prediction error of the network model on the testing dataset is less than 6%, and the prediction speed is two orders faster than that of the numerical simulation. In contrast to the current model, the average error of the network model with the input matrix of the binary map, traditional SDF, and model without pooling layers is around 12%, 11%, and 11%, respectively. The outstanding performance of the proposed network model indicates the potential of the deep neural network-based ROM for real-time control and rapid optimization, while encouraging further investigation to achieve practical application.
Nonlinear proper orthogonal decomposition for convection-dominated flows
Authors: Ahmed Shady E. , San Omer , Rasheed Adil , Iliescu Traian
Published: December 2021

Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven modeling approach for nonlinear dynamical systems when integrated with a time series predictive model. In this Letter, we put forth a nonlinear proper orthogonal decomposition (POD) framework, which is an end-to-end Galerkin-free model combining autoencoders with long short-term memory networks for dynamics. By eliminating the projection error due to the truncation of Galerkin models, a key enabler of the proposed nonintrusive approach is the kinematic construction of a nonlinear mapping between the full-rank expansion of the POD coefficients and the latent space where the dynamics evolve. We test our framework for model reduction of a convection-dominated system, which is generally challenging for reduced order models. Our approach not only improves the accuracy, but also significantly reduces the computational cost of training and testing.
Evaluation of the double-tuned functionally graded thermoelectric material approach for the fabrication of n-type leg based on Pb0.75Sn0.25Te
Authors: Knura Rafal , Parashchuk Taras , Yoshiasa Akira , Wojciechowski Krzysztof T.
Published: December 2021

Thermoelectric (TE) technologies realize the generation of electrical energy from the waste heat. The one bottleneck, which significantly restricts the wide use of these technologies, relates to the low energy conversion efficiency of the commercial devices. In this work, the double-tuned functionally graded thermoelectric material (DT-FGTM) approach was proposed to achieve the high-performance TE leg through the increase in the average TE figure of merit (ZT)ave. The essence of this idea is connected with the precise control of the bandgap Eg and chemical potential μc over the entire temperature range. Considering Pb0.75Sn0.25Te solid solution, as an example, and using the three band Kane model, we evaluated the best conditions for the highest thermoelectric performance in this material. Within the offered herein DT-FGTM approach, we fabricated the thermoelectric n-type Pb0.75Sn0.25Te1−xIx leg and measured its output energy characteristics. The efficiency of energy conversion for the prepared DT-FGTM leg reaches a very high value of ∼12.0% at temperature difference ΔT = 540 K. Furthermore, the thermal treatment of the fabricated leg should not injure the carrier concentration distribution through the leg, as the hot end of the leg is heavily doped, and the chemical diffusion between segments would be only beneficial. Our demonstration shows that the DT-FGTM approach has significant practical interest and can be utilized for the other TE materials.
Black oat (
Authors: Vicensi Marcelo , Umburanas Renan C. , Rampim Leandro , Pott Cristiano A. , de Ávila Fabricio W. , Müller Marcelo M. L.
Published: December 2021

Context The effect of long-term gypsum on nitrogen (N) responsiveness in black oat (Avena strigosa Schreb.) forage lacks information. Aims and methods This study evaluated interactions between different rates of N (0, 50 and 100 kg N ha−1) and gypsum (0, 3, 6, 9 and 12 Mg ha−1) on the yield, nutrition and bromatology of forage black oats in a long-term experiment using a no-till system (NTS) in a Ferralsol in southern Brazil. Key results There was an interaction between residual gypsum and N rate on biomass forage yield in 2015, but not for the 2016 growing season. In 2015, the rate of 100 kg N ha−1 increased yield by 10% in the absence of gypsum and by 30% at low-medium rates of residual-gypsum (3 and 6 Mg ha−1), but there was no response to high N rate under high gypsum rates (9 and 12 Mg ha−1). In 2016, a poor-growing season, a gypsum rate of 6 Mg ha−1 and N rate of 100 kg ha−1 independently improved forage yield by 5%. Gypsum application produced higher forage contents of N, P, K, Ca and S, and decreased Mg content. N-fertilisation increased the N, K, Ca, and Mg contents, but decreased P content and S concentration. Gypsum had no effect on forage bromatological quality, and even with equivalent forage yield between 50 and 100 kg N ha−1, only the high N rate improved some of the forage quality attributes in both years. Conclusions and implications Neither gypsum nor topdressing of N altered N use by black oat forage.
Convergence of undulatory swimming kinematics across a diversity of fishes
Authors: Di Santo Valentina , Goerig Elsa , Wainwright Dylan K. , Akanyeti Otar , Liao James C. , Castro-Santos Theodore , Lauder George V.
Published: December 2021

Significance

Swimming ability has contributed to the evolutionary success of fishes, and its mechanics have been studied extensively. Most fishes swim primarily through undulation of their body and caudal fin (BCF) and have been historically divided into four major kinematic modes based on their morphology. Here, we compare kinematics of BCF locomotion in 44 species. Contrary to expectations and despite considerable morphological diversity, fishes share major kinematic features during steady swimming and are placed on a continuum rather than in discrete categories. This suggests a unifying BCF mechanism to generate efficient aquatic propulsion. Our work reevaluates a well-established hypothesis in biomechanics, highlighting the importance of avoiding a priori partitioning of fishes into modes, to further our understanding of aquatic locomotion.
Efficient mid-infrared wavelength converter based on plasmon-enhanced nonlinear response in graphene nanoribbons
Authors: Chi Jiao , Liu Hongjun , Wang Zhaolu , Huang Nan
Published: December 2021

Graphene plasmons with enhanced localized electric field have been used for boosting the light–matter interaction in linear optical nano-devices. Meanwhile, graphene is an excellent nonlinear material for several third-order nonlinear processes. We present a theoretical investigation of the mechanism of plasmon-enhanced third-order nonlinearity susceptibility of graphene nanoribbons. It is demonstrated that the third-order nonlinearity susceptibility of graphene nanoribbons with excited graphene surface plasmon polaritons can be an order of magnitude larger than the intrinsic susceptibility of a continuous graphene sheet. Combining these properties with the relaxed phase matching condition due to the ultrathin graphene, we propose a novel plasmon-enhanced mid-infrared (MIR) wavelength converter with arrays of graphene nanoribbons. The wavelength of signal light is in the MIR range, which can excite the tunable surface plasmons polaritons in arrays of graphene nanoribbons. The efficiency of the converter from MIR to near-infrared wavelength can be remarkably improved by 60 times compared with a graphene sheet without graphene plasmons. This work provides a novel idea for the efficient application of graphene in nonlinear optical nano-devices. The proposed MIR wavelength converter is compact, tunable and has promising potential in graphene-based MIR detectors with high detection efficiency.
Plasma-catalytic NO
x
production in a three-level coupled rotating electrodes air plasma combined with nano-sized TiO2
Authors: Lei Xinyu , Cheng He , Nie LanLan , Xian YuBin , Lu Xinpei
Published: December 2021

A novel three-level coupled rotating electrodes air plasma with nano-sized TiO2 photocatalysts is developed for plasma-catalytic NO
x
production. The effects of plasma catalysis on NO
x
production with different air flow rates, different N2 fractions and different humidity levels are evaluated. Final results show the exceptional synergistic effect between TiO2 and three-level coupled rotating electrodes air plasma significantly increases the NO
x
concentration by 68.32% (from 4952 to 8335 ppm) and reduces the energy cost by 40.55% (from 2.91 to 1.73 MJ mol−1) at an air flow rate of 12 l min−1 and relative humidity level of 12%, which beats the ideal thermodynamic energy limit ∼2.5 MJ mol−1 for the thermal gas-phase process. A possible mechanism for enhanced NO
x
production with TiO2 is discussed: Highly energetic electrons in plasma contribute to the formations of the electron–hole pairs and oxygen vacancy (Vo) on the TiO2 catalyst surface, which may facilitate the dissociative adsorption of O2 molecules to form superoxide radical groups (like O2
−), and H2O molecules to form surface hydroxyl groups (like OH·), and thus, improving energy efficiency.
Study on Li ion diffusion in LixV2O5 using first principle calculations and kinetic Monte Carlo simulations
Authors: Dietrich Fabian , Cisternas Eduardo , Marcelo Pasinetti Pedro , dos Santos Gonzalo
Published: December 2021

We study the Li diffusion in Li
x
V2O5 (

0
 <
x
⩽
1

)—a potential cathode material for Lithium ion batteries. Different diffusion pathways in this material with dependence on the Li ion concentration are investigated by applying first-principles calculations. The results are used to obtain the corresponding diffusion coefficients by employing two complementary methodologies: Kinetic Monte Carlo (KMC) simulations and a statistical thermodynamics approach. The KMC simulations for two different crystal planes give new evidence that the diffusion occurs mainly along the [010] direction, while the corresponding diffusion coefficients show a temperature dependence obeying Arrhenius’ Law. The necessity of the consideration of concentration-dependent barrier heights in the KMC simulations are demonstrated by looking at the significant changes of the concentration-dependence of the diffusion coefficients. The simulated diffusion coefficients of the combined approach show a good quantitative agreement with experimental data reported previously.
Rare-earth tuned magnetism and magnetocaloric effects in double perovskites R
2NiMnO6
Authors: Ali Anzar , Pasrija Kanika , Sharma Gyaneshwar , Kumar Sanjeev , Singh Yogesh
Published: December 2021

We present a comprehensive experimental study of magnetization (2 < T < 300 K, 1 < H < 8 T) and magnetocaloric effect in double perovskite materials R
2NiMnO6 with R = Pr, Nd, Sm, Gd, Tb, and Dy. While a paramagnetic to ferromagnetic transition, with TC in the range

∼
100
−
200

K, is a common feature that can be attributed to the ordering of Mn4+ and Ni2+ magnetic moments, qualitatively distinct behavior depending on the choice of R is observed at low temperatures. These low-temperature anomalies in magnetization are also manifest in the change in magnetic entropy, −ΔS

M
, whose sign depends on the choice of R. In order to understand these results, we present theoretical analysis based on mean-field approximation and Monte Carlo simulations on a minimal spin model. The model correctly captures the key features of the experimental observations.
Post-nucleation evolution of the liquid–solid interface in nanowire growth
Authors: Maliakkal Carina B , Jacobsson Daniel , Tornberg Marcus , Dick Kimberly A
Published: December 2021

We study using in situ transmission electron microscopy the birth of GaAs nanowires from liquid Au–Ga catalysts on amorphous substrates. Lattice-resolved observations of the starting stages of growth are reported here for the first time. It reveals how the initial nanostructure evolves into a nanowire growing in a zincblende 〈111〉 or the equivalent wurtzite〈0001〉 direction. This growth direction(s) is what is typically observed in most III–V and II–VI nanowires. However, the reason for this preferential nanowire growth along this direction is still a dilemma. Based on the videos recorded shortly after the nucleation of nanowires, we argue that the lower catalyst droplet-nanowire interface energy of the {111} facet when zincblende (or the equivalent {0001} facet in wurtzite) is the reason for this direction selectivity in nanowires.
Plume neutralization of an ionic liquid electrospray thruster: better insights from particle-in-cell modelling
Authors: Zhang Baiyi , Cai Guobiao , He Bijiao , Zhang Kai , Zheng Hongru , Wang Weizong
Published: December 2021

Ionic liquid electrospray thrusters with high specific impulse, high thrust accuracy and low thrust noise are very promising for space gravitational wave detection missions. The plume, which may lead to surface charging of solar panels and sensitive spacecraft components is a great concern for the applications of electrospray thruster. Therefore, this paper investigates the plume neutralization process of the ionic liquid electrospray thruster through fully kinetic particle-in-cell simulations. The unipolarity operation mode is firstly simulated and compared with the experimental measurements. The bipolar operation mode is analyzed by considering the premixing and the separation of positive and negative ion beams. At the same time, the effect of beam spacing on the plume characteristics is investigated. The results show that the plume neutralization of the ionic liquid electrospray thruster is achieved by the spatial and temporal oscillations of the ion beams. In the horizontal direction, the spatial oscillations are caused by the different mass and hence velocities of positive and negative ions. In the vertical direction, the spatial oscillations are mainly because of the non-zero beam spacing. The temporal oscillations may be related to the tradition plasma oscillation. As the beam spacing increases, the oscillation amplitude of the horizontal electric potential curve changes scarcely; however, in the vertical direction, the oscillation amplitude of the curve increases. The ion temperature goes up with the beam spacing and the deviation of the temperature of beam ions does not exceed 15 eV in the horizontal direction but exceeds 100 eV in the vertical direction. Moreover, the plume divergence half angle and the beam spacing are positively correlated, suggesting that the ionic electrospray thrusters with positive and negative polarity need to be placed as close as possible in the spacecraft.
Nanoscale wafer patterning using SPM induced local anodic oxidation in InP substrates
Authors: Ovenden Charlotte , Farrer Ian , Skolnick Maurice S , Heffernan Jon
Published: December 2021

Atomic force microscopy (AFM) assisted local anodic oxidation (LAO) offers advantages over other semiconductor fabrication techniques as it is a low contamination method. We demonstrate the fabrication of deep and highly reproducible nanohole arrays on InP using LAO. Nanohole and nano-oxide mound radius and depth are controlled independently by altering AFM tip bias and humidity, with a maximum nanohole depth of 15.6 ± 1.2 nm being achieved. Additionally, the effect of tip write speed on oxide line formation is compared for n-type, p-type and semi-insulating substrates, which shows that n-type InP oxidizes at a slower rate that semi-insulated or p-type InP. Finally, we calculate the activation energy for LAO of semi-insulating InP to be 0.4 eV, suggesting the oxidation mechanism is similar to that which occurs during plasma oxidation.
Dual-mode dendritic devices enhanced neural network based on electrolyte gated transistors
Authors: Jing Zhaokun , Yang Yuchao , Huang Ru
Published: December 2021

As a fundamental component of biological neurons, dendrites have been proven to have crucial effects in neuronal activities. Single neurons with dendrite structures show high signal processing capability that is analogous to a multilayer perceptron (MLP), whereas oversimplified point neuron models are still prevalent in artificial intelligence algorithms and neuromorphic systems and fundamentally limit their efficiency and functionality of the systems constructed. In this study, we propose a dual-mode dendritic device based on electrolyte gated transistor, which can be operated to generate both supralinear and sublinear current–voltage responses when receiving input voltage pulses. We propose and demonstrate that the dual-mode dendritic devices can be used as a dendritic processing block between weight matrices and output neurons so as to dramatically enhance the expression ability of the neural networks. A dual-mode dendrites-enhanced neural network is therefore constructed with only two trainable parameters in the second layer, thus achieving 1000× reduction in the amount of second layer parameter compared to MLP. After training by back propagation, the network reaches 90.1% accuracy in MNIST handwritten digits classification, showing advantage of the present dual-mode dendritic devices in building highly efficient neuromorphic computing.
Parametric study of high-energy ring-shaped electron beams from a laser wakefield accelerator
Authors: Maitrallain A , Brunetti E , Streeter M J V , Kettle B , Spesyvtsev R , Vieux G , Shahzad M , Ersfeld B , Yoffe S R , Kornaszewski A , Finlay O , Ma Y , Albert F , Bourgeois N , Dann S J D , Lemos N , Cipiccia S , Cole J M , González I G , Willingale L , Higginbotham A , Hussein A E , Šmid M , Falk K , Krushelnick K , Lopes N C , Gerstmayr E , Lumsdon C , Lundh O , Mangles S P D , Najmudin Z , Rajeev P P , Symes D R , Thomas A G R , Jaroszynski D A
Published: December 2021

Laser wakefield accelerators commonly produce on-axis, low-divergence, high-energy electron beams. However, a high charge, annular shaped beam can be trapped outside the bubble and accelerated to high energies. Here we present a parametric study on the production of low-energy-spread, ultra-relativistic electron ring beams in a two-stage gas cell. Ring-shaped beams with energies higher than 750 MeV are observed simultaneously with on axis, continuously injected electrons. Often multiple ring shaped beams with different energies are produced and parametric studies to control the generation and properties of these structures were conducted. Particle tracking and particle-in-cell simulations are used to determine properties of these beams and investigate how they are formed and trapped outside the bubble by the wake produced by on-axis injected electrons. These unusual femtosecond duration, high-charge, high-energy, ring electron beams may find use in beam driven plasma wakefield accelerators and radiation sources.
Nonreciprocal two-photon transmission and statistics in a chiral waveguide QED system
Authors: Wang Lei , Yi Zhen , Sun Li-Hui , Gu Wen-Ju
Published: December 2021

We study the nonreciprocal properties of transmitted photons in a chiral waveguide quantum electrodynamics (QED) system, including single- and two-photon transmissions and second-order correlations. For the single-photon transmission, the nonreciprocity is induced by the effects of chiral coupling and atomic dissipation in the weak coupling region. It vanishes in the strong coupling regime when the effect of atomic dissipation becomes ignorable. In the case of two-photon transmission, there exist two ways of going through the emitter: independently as plane waves and formation of bound state. Besides the nonreciprocal behavior of plane waves, the bound state that differs in two directions also alters transmission probabilities. In addition, the second-order correlation of transmitted photons depends on the interference between plane wave and bound state. The destructive interference leads to the strong antibunching in the weak coupling region, while the effective formation of bound state leads to the strong bunching in the intermediate coupling region. However, the negligible interactions for left-propagating photons hardly change the statistics of the input coherent state.
Agglomerative likelihood clustering
Authors: Yelibi Lionel , Gebbie Tim
Published: December 2021

We consider the problem of fast time-series data clustering. Building on previous work modeling, the correlation-based Hamiltonian of spin variables we present an updated fast non-expensive agglomerative likelihood clustering algorithm (ALC). The method replaces the optimized genetic algorithm based approach (f-SPC) with an agglomerative recursive merging framework inspired by previous work in econophysics and community detection. The method is tested on noisy synthetic correlated time-series datasets with a built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results. We apply it to time-series datasets as large as 20 000 assets and we argue that ALC can reduce computation time costs and resource usage costs for large scale clustering for time-series applications while being serialized, and hence has no obvious parallelization requirement. The algorithm can be an effective choice for state-detection for online learning in a fast non-linear data environment, because the algorithm requires no prior information about the number of clusters.
Competences of teachers of natural-physical sciences. An analysis from the perception of the students
Authors: Prada-Núñez R , Ayala E T , Avendaño-Castro W R
Published: December 2021

This article arises as a proposal in view of the need to evaluate the scientific competences promoted by teachers of the subject of physics at the level of basic secondary and secondary technical education. A valid questionnaire was designed from the application of scalar analysis, factorial analysis and content analysis, which is composed of 49 items evaluated by means of a Likert scale with five levels of response. It was applied in a sample of 249 students enrolled in a public educational institution during 2019, characterized by their good results in the area of physics in state tests. The results allowed the identification of strengths in the four dimensions proposed by the Ministerio de Educación Nacional, Colombia (pedagogical, didactic, disciplinary and behavioural), in contrast with some weaknesses within which the evaluation process stands out as the one with the greatest impact, since the students state that this process is assumed by the teacher as a mechanism of pressure and control. When investigating the teachers in a complementary way, positions were determined that were totally opposite to those held by the students, then it is suggested for future research to consider both the students and the teachers as informants and a supervision of the students’ notes as the end of triangulating the results to refine the conclusions, on which future improvement plans will depend.
Design of Controller for Electrical Vehicle to Grid Power
Authors: Baghmare K K , Daigavane P M
Published: December 2021

This paper prescribed the design of controller for electrical vehicle to Grid power, by using this controller improve the power requirement of grid and reactive power compensation capability. Bidirectional converter is very helpful during on peak load demand. During off peak load demand grid will supply the power to the battery and charge the battery. During on peak load demand excess power of battery will supply to the grid. The concept aggregator is depicted in the figure 2. (Aggregator collects the power from all electrical vehicle first then it supply to the grid). This modern electrical vehicle technology proposed the distribution generation Methodology. All the control strategies of modern electrical vehicle to grid is proposed like smart charging or discharging of batteries during off peak load demand and On peak load demand respectively. V2G controller allow the active power it act as an ancillary services to grid. Electrical vehicle controller has ability to exchange the active or reactive power capability. Simulation of bidirectional AC/DC and DC/DC controller and their control circuit are analyzed by using matlab Simulink software.
Image Anonymization using Deep Convolutional Generative Adversarial Network
Authors: Rao K Nitalaksheswara , Jayasree P , Krishna Ch.V.Murali , Sai Prasanth K , Reddy Ch Satyananda
Published: December 2021

Advancement in deep learning requires significantly huge amount of data for training purpose, where protection of individual data plays a key role in data privacy and publication. Recent developments in deep learning demonstarte a huge challenge for traditionally used approch for image anonymization, such as model inversion attack, where adversary repeatedly query the model, inorder to reconstrut the original image from the anonymized image. In order to apply more protection on image anonymization, an approach is presented here to convert the input (raw) image into a new synthetic image by applying optimized noise to the latent space representation (LSR) of the original image. The synthetic image is anonymized by adding well designed noise calculated over the gradient during the learning process, where the resultant image is both realistic and immune to model inversion attack. More presicely, we extend the approach proposed by T. Kim and J. Yang, 2019 by using Deep Convolutional Generative Adversarial Network (DCGAN) in order to make the approach more efficient. Our aim is to improve the efficiency of the model by changing the loss function to achieve optimal privacy in less time and computation. Finally, the proposed approach is demonstrated using a benchmark dataset. The experimental study presents that the proposed method can efficiently convert the input image into another synthetic image which is of high quality as well as immune to model inversion attack.
Multi-class classification for large datasets with optimized SVM by non-linear kernel function
Authors: Sunitha Lingam , Raju M Bal
Published: December 2021

Most important part of Support Vector Machines(SVM) are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help improve the accuracy of SVM. The proposed work aims to develop a new kernel function for a multi-class support vector machine, perform experiments on various data sets, and compare them with other classification methods. Directly it is not possible multiclass classification with SVM. In this proposed work first designed a model for binary class then extended with the one-verses-all approach. Experimental results have proved the efficiency of the new kernel function. The proposed kernel reduces misclassification and time. Other classification methods observed better results for some data sets collected from the UCI repository.
Designing of Lithium - Ion Battery Pack Rechargeable on a Hybrid System with Battery Management System (BMS) for DC Loads of Low Power Applications – A Prototype Model
Authors: Bhukya Ramu , Nalli Praveen Kumar , Kadali Kalyan Sagar , Bade Mahendra Chand
Published: December 2021

Now a days, Li-ion batteries are quite possibly the most exceptional battery-powered batteries; these are drawing in much consideration from recent many years. M Whittingham first proposed lithium-ion battery technology in the 1970s, using titanium sulphide for the cathode and lithium metal for the anode. Li-ion batteries are the force to be reckoned with for the advanced electronic upset in this cutting-edge versatile society, solely utilized in cell phones and PC computers. A battery is a Pack of cells organized in an arrangement/equal association so the voltage can be raised to the craving levels. Lithium-ion batteries, which are completely utilised in portable gadgets & electric vehicles, are the driving force behind the digital technological revolution in today’s mobile societies. In order to protect and maintain voltage and current of the battery with in safe limit Battery Management System (BMS) should be used. BMS provides thermal management to the battery, safeguarding it against over and under temperature and also during short circuit conditions. The battery pack is designed with series and parallel connected cells of 3.7v to produce 12v. The charging and releasing levels of the battery pack is indicated by interfacing the Arduino microcontroller. The entire equipment is placed in a fiber glass case (looks like aquarium) in order to protect the battery from external hazards to design an efficient Lithium-ion battery by using Battery Management System (BMS). We give the supply to the battery from solar panel and in the absence of this, from a regular AC supply.
Uplink and Downlink Variation in Drone Technology for Cloud, Edge, Fog and Smart Dust Integrated IoT Architecture: Demonstrated Over WSNs
Authors: Dorave Jayantkumar , Sadiwala Ritesh
Published: December 2021

IoT concepts are heavily applicable in drone communication integrated with different network architecture for optimization. Distributing the burden allows more IoT devices to execute calculations, rather than everything being done on the cloud. There are numerous IoT designs that have emerged as a result of this. By relocating calculations away from the cloud, these designs make use of the enhanced processing capacity of the devices. Based on our needs, we’ve limited it down to four architectures, each of which we have discussed for optimized flow useful in drone technology. We have also applied the one live dataset for the test drone using raspberry pi processor system powered with for end-to-end drone communication establishment. The analysis of downlink and uplink were studied for time analysis for IoT architecture using drone cell characteristics. New technology makes it possible to implement drone cell (DC) connectivity, which is highly flexible and cost-effective for the gathering of Internet-of-things (IoT) data when terrestrial networks are not yet accessible. DC’s flight path has a substantial impact on data collecting systems.
Experimental investigation of bending characteristics of hybrid composites fabricated by hand layup method
Authors: Sadashiva M , Praveen Kumar S , Yathish M K , Satish V T , Srinivasa MR , Sharanraj V
Published: December 2021

The extensive applications of hybrid composite materials in the field of transportation and structural domine provide prominent advantages in the order of stiffness, strength even cost. However extend the advantages of hybrid campsites in several field such as aviation and marine even more additional properties should be inculcate in them. During production of such profitable composites poses some problems at time at decompose and processing. It’s better to develop environment friendly and reusable composites, bio hybrid composite materials such of the one. In this paper, focused on development of Eco-friendly hybrid bio composites with the ingredients of drumstick fibers, glass fiber along with polyester resin. This hybrid bio composites subjected to bending test and evaluate the characteristics of bending properties, this research evident that bending characteristics of hybrid composites with longitudinal fiber orientation better than transverse.
New Approach to Find Fixed Point in Extended b-metric Space
Authors: Gonder Surjeet Singh Chauhan , Rana Kanika
Published: December 2021

A fixed point for a suitable map or operator is identical to the presence of a solution to a theoretical or real-world problem. As a result, fixed points are crucial in many fields of mathematics, science, and engineering. In this paper, we establish new fixed point results on self-mappings in setting of extended b-metric space which can be extended further to give application in real world such as in image processing, computer graphics, Nash equilibrium and many more. Our results extends the corresponding results of Mukheimer et. al. [Aimal Mukheimer, Nabil Mlaiki, Kamal Abodayeh, Wasfi Shantanawi, Non Linear Analysis: Modeling and Control, 24(6), 870-883, 2019.] and Kamran et. al. [Tayyab Kamran, Maria Samreen, Qurrat UL Ain, Mathematics, 5(19), 2017, 7 pages.]. Examples are also mentioned to check the authenticity of our results. A solution to Fredholm integral equation is also demonstrated as an application.
A model for early diagnosis of Cardiac Autonomic Neuropathy (CAN)
Authors: Sharanya S. , Sridhar P.A.
Published: December 2021

Convolutional Neural Networks (CNN) are widely used as prediction models in medical diagnosis in the recent research. Remodelling the CNN architecture to make it more reliable for classification is the core of each finding. Cardiac Autonomic Neuropathy (CAN) is a severity amongst the diabetic population, who are subject to diabetes for long duration. The aim of this work is to provide a predictive mechanism that is designed for more reliable diagnostics by studying the ECG physiology and enhancing the diagnostics by artificial technique, like using a remodelled CNN architecture. Results of CNN show 95.42 % efficiency in classifying between groups of CAN+ and CAN-groups..
Assistive Robot For Visually Impaired People
Authors: Sunitha K.A. , Suraj Ganti Sri Giri Sai , Atchyut Sriram G , Savitha Sai N
Published: December 2021

The proposed robot aims to serve as a personal assistant for visually impaired people in obstacle avoidance, in identifying the person (known or unknown) with whom they are interacting with and in navigating. The robot has a special feature in truly locating the subject’s location using GPS. The novel feature of this robot is to identify people with whom the subject interacts. Facial detection and identification in real-time has been a challenge and achieved with accurate image processing using viola jones and SURF algorithms. An obstacle avoidance design has been implanted in the system with many sensors to guide in the correct path. Hence, the robot is a fusion of providing the best of the comfort and safety with minimal cost.
Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network
Authors: Giriprasad Gaddam P. , Sanjeeva reddy A , Sreehari R.V.
Published: December 2021

In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool
IoT based Automation of Public Garden and Botanical Garden
Authors: Pithadiya B H , Parikh H N , Pandya H N , Vyas D
Published: December 2021

People in urban areas appreciate and enjoy the beauty of nature as carefully manicured grass and flowers in public gardens. Senior citizens find this place best for their social networking, while children enjoy their recreational activities here. Botanical garden generally refers to a place where, variety of flora species are planted and grown for the purpose of scientific study. Maintaining a garden or botanical garden takes lot of maintenance effort along with monitoring various parameters such as moisture and light; ambient factors like temperature and humidity of air are also important factors. The focus of this paper is to demonstrate and implement a cost-effective way to monitor and control soil moisture, ambient air temperature and humidity in such gardens by means of controlling drip irrigation pumps and water sprinklers.
Design and Implementation of AGU based FFT Pipeline Architecture
Authors: Prasanna Kumar G. , Chandra Maturi Sarath , Shiva Prasanna K , Mahesh M
Published: December 2021

Present it is most needful task to get various applications with parallel computations by using a Fast Fourier Transform (FFT) and the derived outputs should be in regular format. This can be achieved by using an advanced technique called Multipath delay commutator (MDC) Pipelining FFT processor and this processor will be capable to perform the computation of a different data streams at a time. In this paper the design and implementation of AGU based Pipelined FFT architecture is done Caluclation of a butterfly is done within 2 cycles by the instructions proposed. A Data Processing Unit (DPU) is employed in this pipeline architecture and supports the instructions & an FFT Adress Generation Unit (FAGU) caluclates butterfly input & output data adresses automatically. The DPU proposed sysyem requires less area compared to commericial DSP chips. Futhermore, the proposed FAGU reduces the number of FFT computation cycles. The FFT design architecture will have real data paths. With various FFT sizes, different radix & various parallesim levels, the FFT can be mapped to the pipeline architecture. The most attractive feature of the pipelined FFT architecture is it consists of bit reversal operation so it requires little number of registers and better throughput.
Development and validation of an expert system for lubricant based predictive maintenance of machinery
Authors: Indra Reddy A , Ramana K V
Published: December 2021

Predictive maintenance is also known as condition monitoring. It is a technique in which systems are monitored at various key points of the operating machinery and data is observed. Analyzing the data is very much useful to assess the health of the machine thereby catastrophic failures can be avoided. This leads to more production and also eliminate to a great extent, the inventory. To make the technique more convenient, the expert system is very much useful. In this paper an attempt has been made to take up a comprehensive review of the literature and emphasis has been given to lubricant condition-based monitoring through an expert system, which will spell out the probable faults along with remedial measures. The expert system embeds the viscosity of lubricant as measurand. It has been developed for different grades of lubricating oils and their industrial utility.
A Fusion Of Sensors Information On Path Tracking For Autonomous Driving Control Of An Electric Vehicle (EV)
Authors: Haris Hasri , Khairunizam Wan , Halin Hafiz , Mustafa Wan Azani , AB Shahriman , Rzalan Zuradman M.
Published: December 2021

Depending on an intellectual level and experience, each human may make judgments and respond to situations autonomously. The driver is alerted and knows what to do in a specific circumstance while driving. This research aims to see how individuals act when driving an electric car down a predetermined path. An electric buggy car is built with equipment and sensors called an Electric Vehicle (EV) in experiments. Individuals who meet specified requirements are chosen to analyse their driving behaviours, and data is collected using various sensors. The speed, steering wheel angle, heading, and position of the buggy car are recorded throughout the human navigation trials. After the tests, data on human behaviour while driving straight and turning left and right are collected.
Smart Assistive Shoes with Internet of Things Implementation for Visually Impaired People
Authors: Kamaruddin F S , Mahmood N H , Abdul Razak M A , Zakaria N A
Published: December 2021

Visually impaired people usually have a lot of difficulties involved in interacting with their environment. Physical movement is a major challenge for them, because it can be tricky to make a distinction about where they are and how they can move from one place to another. In this project, smart assistive shoes with Internet of Things (IoT) implementation is designed. These shoes are equipped with ultrasonic sensors and vibration motors that can warn users about obstacles. Next, the IoT system is implemented using Adafruit IO and If This, Then That (IFTTT) to transfer data between Google Assistant and buzzer for shoes position finder purposes. NodeMCU allows the buzzer on shoes to be controlled by the Internet using its WiFi module which is connected to the mobile phone hotspots. As a result, shoes with an obstacle detection system which can detect obstacles within 20 cm distance and shoes position finder using Google Assistant are designed. In conclusion, hopefully these shoes will become one of the alternatives to aid the independent movement of the visually impaired people in the future.
Malaria Parasite Diagnosis Using Computational Techniques: A Comprehensive Review
Authors: Mustafa Wan Azani , Alquran Hiam , Aihsan Muhammad Zaid , Saifizi Mohd , Khairunizam Wan , Abdul-Nasir Aimi Salihah , Abdul Kader Mohamed Mydin M. , Salimi Midhat Nabil , Nasrudin Mohd Wafi
Published: December 2021

Malaria is a very serious disease that caused by the transmitted of parasites through the bites of infected Anopheles mosquito. Malaria death cases can be reduced and prevented through early diagnosis and prompt treatment. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. Currently, microscopy-based diagnosis remains the most widely used approach for malaria diagnosis. The development of automated malaria detection techniques is still a field of interest. Automated detection is faster and high accuracy compared to the traditional technique using microscopy. This paper presents an exhaustive review of these studies and suggests a direction for future developments of the malaria detection techniques. This paper analysis of three popular computational approaches which is k-mean clustering, neural network, and morphological approach was presented. Based on overall performance, many research proposed based on the morphological approach in order to detect malaria.