bluuebunny/crossref_metadata_2025 huggingface dataset:
n-decane diffusion in carbon nanotubes with vibration
Authors: Chen Zhongliang , Dong Xiaohu , Chen Zhangxin
Published: February 2021

Carbon nanotubes (CNTs) have a wide range of applications in nanotechnology engineering. This research aims to quantify the effect of wall vibration on n-decane molecules’ diffusion in double-walled CNTs (DWNTs) with different diameters and determine the diffusion mechanisms behind it. Molecular dynamics simulations are performed to generate mass density profiles of confined n-decane molecules. The root mean square fluctuation and mean squared displacement analyses show that the confinement suppresses n-decane molecules’ fluctuations. A self-diffusion coefficient of n-decane molecules in a 13.6 Å-diameter DWNT is the largest. However, the vibration enhancement of the n-decane molecules’ diffusion in a 27.1 Å-diameter DWNT is 207%, more extensive than that in 13.6 Å-diameter and 10.8 Å-diameter DWNTs. The n-decane–CNT attractive interactions, extreme confinement, and surface friction affect the n-decane molecules’ diffusion in CNTs with vibration.
A Rosetta-based protein design protocol converging to natural sequences
Authors: Sormani Giulia , Harteveld Zander , Rosset Stéphane , Correia Bruno , Laio Alessandro
Published: February 2021

Computational protein design has emerged as a powerful tool capable of identifying sequences compatible with pre-defined protein structures. The sequence design protocols, implemented in the Rosetta suite, have become widely used in the protein engineering community. To understand the strengths and limitations of the Rosetta design framework, we tested several design protocols on two distinct folds (SH3-1 and Ubiquitin). The sequence optimization, when started from native structures and natural sequences or polyvaline sequences, converges to sequences that are not recognized as belonging to the fold family of the target protein by standard bioinformatic tools, such as BLAST and Hmmer. The sequences generated from both starting conditions (native and polyvaline) are instead very similar to each other and recognized by Hmmer as belonging to the same “family.” This demonstrates the capability of Rosetta to converge to similar sequences, even when sampling from distinct starting conditions, but, on the other hand, shows intrinsic inaccuracy of the scoring function that drifts toward sequences that lack identifiable natural sequence signatures. To address this problem, we developed a protocol embedding Rosetta Design simulations in a genetic algorithm, in which the sequence search is biased to converge to sequences that exist in nature. This protocol allows us to obtain sequences that have recognizable natural sequence signatures and, experimentally, the designed proteins are biochemically well behaved and thermodynamically stable.
Sampling initial positions and momenta for nuclear trajectories from quantum mechanical distributions
Authors: Yao Yuxuan , Hase William L. , Granucci Giovanni , Persico Maurizio
Published: February 2021

We compare algorithms to sample initial positions and momenta of a molecular system for classical trajectory simulations. We aim at reproducing the phase space quantum distribution for a vibrational eigenstate, as in Wigner theory. Moreover, we address the issue of controlling the total energy and the energy partition among the vibrational modes. In fact, Wigner’s energy distributions are very broad, quite at variance with quantum eigenenergies. Many molecular processes depend sharply on the available energy, so a better energy definition is important. Two approaches are introduced and tested: the first consists in constraining the total energy of each trajectory to equal the quantum eigenenergy. The second approach modifies the phase space distribution so as to reduce the deviation of the single mode energies from the correct quantum values. A combination of the two approaches is also presented.
Experimental evidence of selective generation and one-way conversion of parities in valley sonic crystals
Authors: Geng Zhi-Guo , Zeng Long-Sheng , Shen Ya-Xi , Peng Yu-Gui , Zhu Xue-Feng
Published: February 2021

Valley pseudo-spin and its associated interface wave transport in sonic crystals has attracted increasing attention from researchers for the potential manipulation of acoustic waves. The topological interface state, projected from a specific valley, is valley-locked, and, thus, renders robust reflection immunity against defects. In this work, we report on the experimental observation of the different parity generations of interface states at two distinct zigzag interfaces. By designing a “C”-shaped domain wall, we experimentally demonstrated the parity generation and selective excitation of interface valley-locked states. Benefiting from different parities of the interface states, one-way valley parity conversion was verified in sonic crystals without breaking the time reversal symmetry. Our findings contribute to the applications in noise control, acoustic communication, and logic processing for topological functional devices with unidirectional responses.
Formation of negative and positive ions in the radiosensitizer nimorazole upon low-energy electron collisions
Authors: Meißner R. , Feketeová L. , Bayer A. , Limão-Vieira P. , Denifl S.
Published: February 2021

A comprehensive investigation of low-energy electron attachment and electron ionization of the nimorazole radiosensitizer used in cancer radiation therapy is reported by means of a gas-phase crossed beam experiment in an electron energy range from 0 eV to 70 eV. Regarding negative ion formation, we discuss the formation of fifteen fragment anions in the electron energy range of 0 eV–10 eV, where the most intense signal is assigned to the nitrogen dioxide anion NO2−. The other fragment anions have been assigned to form predominantly from a common temporary negative ion state close to 3 eV of the nitroimidazole moiety, while the morpholine moiety seems to act only as a spectator in the dissociative electron attachment event to nimorazole. Quantum chemical calculations have been performed to help interpreting the experimental data with thermochemical thresholds, electron affinities, and geometries of some of the neutral molecules. As far as positive ion formation is concerned, the mass spectrum at the electron energy of 70 eV shows a weakly abundant parent ion and C5H10NO+ as the most abundant fragment cation. We report appearance energy (AE) measurements for six cations. For the intact nimorazole molecular cation, the AE of 8.16 ± 0.05 eV was obtained, which is near the presently calculated adiabatic ionization energy.
Optimal choice of multiple line-of-sight measurements determining plasma hotspot velocity at the National Ignition Facility
Authors: Hartouni E. P. , Bionta R. M. , Eckart M. J. , Field J. E. , Grim G. P. , Hahn K. D. , Hatarik R. , Jeet J. , Kerr S. M. , Libby S. B. , Moore A. S. , Munro D. H. , Schlossberg D. J.
Published: February 2021

The measurement of plasma hotspot velocity provides an important diagnostic of implosion performance for inertial confinement fusion experiments at the National Ignition Facility. The shift of the fusion product neutron mean kinetic energy as measured along multiple line-of-sight time-of-flight spectrometers provides velocity vector components from which the hotspot velocity is inferred. Multiple measurements improve the hotspot velocity inference; however, practical considerations of available space, operational overhead, and instrumentation costs limit the number of possible line-of-sight measurements. We propose a solution to this classical “experiment design” problem that optimizes the precision of the velocity inference for a limited number of measurements.
A multiple pin Langmuir probe for the measurement of the electric field drift and its spatial derivatives
Authors: Barni R. , Alex P. , Riccardi C.
Published: February 2021

We discuss the measurement of the electric field drift (E→xB→ velocity) and its spatial derivatives in a low temperature magnetized plasma by means of a suitably arranged multi-pin Langmuir probe. Results are presented relating the properties of the velocity field and its electrostatic fluctuations. In particular, we have measured components of the non-linear terms in the fluid magnetohydrodynamics equation.
Multi-energy calibration of a PILATUS3 CdTe detector for hard x-ray measurements of magnetically confined fusion plasmas
Authors: Barbui T. , Delgado-Aparicio L. F. , Pablant N. , Disch C. , Luethi B. , Pilet N. , Stratton B. , VanMeter P.
Published: February 2021

A multi-energy hard x-ray pin-hole camera based on the PILATUS3 X 100K-M CdTe detector has been developed at the Princeton Plasma Physics Laboratory for installation on the Tungsten Environment in Steady State Tokamak. This camera will be employed to study thermal plasma features such as electron temperature as well as non-thermal effects such as fast electron tails produced by a lower hybrid radiofrequency current drive and the birth of runaway electrons. The innovative aspect of the system lies in the possibility of setting the threshold energy independently for each of the ∼100k pixels of the detector. This feature allows for the measurement of the x-ray emission in multiple energy ranges with adequate space and time resolution (∼1 cm, 2 ms) and coarse energy resolution. In this work, the energy dependence of each pixel was calibrated within the range 15 keV–100 keV using a tungsten x-ray tube and emission from a variety of fluorescence targets (from yttrium to uranium). The data corresponding to pairs of Kα emission lines are fit to the characteristic responsivity (“S-curve”), which describes the detector sensitivity across the 64 possible energy threshold values for each pixel; this novel capability is explored by fine-tuning the voltage of a six-bit digital-analog converter after the charge-sensitive amplifier for each of the ∼100k pixels. This work presents the results of the calibration including a statistical analysis. It was found that the achievable energy resolution is mainly limited by the width of the S-curve to 3 keV–10 keV for threshold energies up to 50 keV, and to ≥20 keV for energies above 60 keV.
Synchronization of wave structures in a heterogeneous multiplex network of 2D lattices with attractive and repulsive intra-layer coupling
Authors: Shepelev I. A. , Muni S. S. , Vadivasova T. E.
Published: February 2021

We explore numerically the synchronization effects in a heterogeneous two-layer network of two-dimensional (2D) lattices of van der Pol oscillators. The inter-layer coupling of the multiplex network has an attractive character. One layer of 2D lattices is characterized by attractive coupling of oscillators and demonstrates a spiral wave regime for both local and nonlocal interactions. The oscillators in the second layer are coupled through active elements and the interaction between them has repulsive character. We show that the lattice with the repulsive type of coupling demonstrates complex spatiotemporal cluster structures, which can be called labyrinth-like structures. We show for the first time that this multiplex network with fundamentally various types of intra-layer coupling demonstrates mutual synchronization and a competition between two types of structures. Our numerical study indicates that the synchronization threshold and the type of spatiotemporal patterns in both layers strongly depend on the ratio of the intra-layer coupling strength of the two lattices. We also analyze the impact of intra-layer coupling ranges on the synchronization effects.
Prediction of fish mortality based on a probabilistic anomaly detection approach for recirculating aquaculture system facilities
Authors: Lopes Bruna D. M. , Silva Luís C. B. , Blanquet Isidro M. , Georgieva Pétia , Marques Carlos A. F.
Published: February 2021

Aquaculture is a fundamental sector of the food industry nowadays. However, to become a sustainable and more profitable industry, it is necessary to monitor several associated parameters, such as temperature, salinity, ammonia, potential of hydrogen, nitrogen dioxide, bromine, among others. Their regular and simultaneous monitoring is expected to predict and avoid catastrophes, such as abnormal fish mortality rates. In this paper, we propose a novel anomaly detection approach for the early prediction of high fish mortality based on a multivariate Gaussian probability model. The goal of this approach is to determine the correlation between the number of daily registered physicochemical parameters of the fish tank water and the fish mortality. The proposed machine learning model was fitted with data from the weaning and pre-fattening phases of Senegalese sole (Solea senegalensis) collected over 2018, 2019, and 2020. This approach is suitable for real-time tracking and successful prediction of up to 80% of the high fish mortality rates. To the best of our knowledge, the proposed anomaly detection approach is the first time studied and applied in the framework of the aquaculture industry.
Effects of swift heavy ion-irradiation on magnetic properties of Co-doped TiO2
Authors: Kumar S. R. , Phase D. M. , Gupta Ajay , Pandey A. C. , Gupta Ratnesh
Published: February 2021

Ferromagnetic Co-doped TiO2 thin films at room temperature have been synthesized by RF sputtering and ion-irradiated with 100 MeV Au ions. The surface roughness of the film increases with increasing Au-ion fluence. Kerr microscopy measurements at room temperature give isotropic hysteresis loop with negligible coercivity, which gets modified into in-plane uniaxial magnetic anisotropy after anion fluence of 5×1011 ions/cm2. Further ion-irradiation reduced the coercivity of the film. X-ray magnetic circular dichroism (XMCD) measurements have been performed in total electron yield mode, to investigate the effects of ion-irradiation on magnetic properties of the Co-doped TiO2 thin films. From the XMCD analysis, we observed that the total magnetic moment of Co atoms in case of as-deposited film is 1.771±0.016μB. After the ion-irradiation it became 0.3818±0.014μB at room temperature.
Metabolomics analysis reveals metabolic changes associated with trans-resveratrol treatment in experimental cryptorchidism mice
Authors: Li Siqiang , Li Yun , Chen Fujia , Yang Yurong , Song Li , Liu Chaoying , Wang Baogen , Xu Yuanhong , Shao Mingguang , Li Enzhong
Published: February 2021

This study aimed to analyse global metabolomic changes associated with trans-resveratrol (RSV) treatment in mice with cryptorchidism using untargeted metabolomics. Cryptorchidism was established surgically in Kunming mice, which were then treated with 20µg g–1 day–1, s.c., RSV for 35 consecutive days. Typical manifestations of spermatogenesis arrest were seen in mice with cryptorchidism, and RSV treatment for 35 days restored spermatogenesis. Liquid chromatography–tandem mass spectrometry was used to profile the metabolome of testes from mice in the control (non-cryptorchid, untreated), cryptorchid and RSV-treated cryptorchid groups. In all, 1386 and 179 differential metabolites were detected in the positive and negative modes respectively. Seven and six potential biomarkers were screened for spermatogenesis arrest and restoration respectively. Pathway analysis showed changes in 197 metabolic pathways. The hexosamine biosynthesis pathway was inhibited in the cryptorchid group, which probably resulted in a decrease in the end product, uridine diphosphate N-acetylglucosamine (UDP-GlcNAc). Immunoblot analysis showed that total testicular protein O-linked β-N-acetylglucosamine glycosylation was related to spermatogenesis arrest, further indicating a decrease in UDP-GlcNAc in the cryptorchid group. Thus, untargeted metabolomics revealed the biochemical pathways associated with the restoration of metabolic status in the cryptorchid group following RSV treatment and the findings could be used to monitor the response to RSV treatment. This study provides a meaningful foundation for the future clinical application of RSV in the treatment of spermatogenesis dysfunction.
Machine learning for magnetic phase diagrams and inverse scattering problems
Authors: Samarakoon Anjana M , Alan Tennant D
Published: February 2021

Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo methods. These approaches are shown to be effective at simulating magnetic structures and dynamics in a wide range of materials. Using large numbers of simulations the effectiveness of machine learning approaches are assessed. Principal component analysis and nonlinear autoencoders are considered with the latter found to provide a high degree of compression and to be highly suited to neutron scattering problems. Agglomerative heirarchical clustering in the latent space is shown to be effective at extracting phase diagrams of behavior and features in an automated way that aid understanding and interpretation. The autoencoders are also well suited to optimizing model parameters and were found to be highly advantageous over conventional fitting approaches including being tolerant of artifacts in untreated data. The potential of machine learning to automate complex data analysis tasks including the inversion of neutron scattering data into models and the processing of large volumes of multidimensional data is assessed. Directions for future developments are considered and machine learning argued to have high potential for impact on neutron science generally.
Interferometric device for the in-process measurement of diameter variation in the manufacture of ultraprecise spheres
Authors: Meeß Rudolf , Dontsov Dennis , Langlotz Enrico
Published: February 2021

A novel device is presented which is designed for in-process measurements of the variation of the diameter of highly reflective spheres. Silicon spheres have been used for the new definition of the International System of Units (SI). Many spheres have to be processed, and the form of these objects, and thus the manufacturing process’s stability, needs to be controlled every day. Commercially available measurement equipment and even state-of-the-art spherical interferometers have reached their limits in terms of resolution, uncertainty, the complexity of their handling routines, measurement time and even financial investment. A novel setup has thus been designed after considering and selecting a special mechanical setup with a minimal measurement loop, stable optical sensors and a handling strategy which avoids collision and contact with the very valuable, superpolished spherical objects. Thus, the design minimizes the influence of the environment and reduces the measurement time at an equator with sub-nanometre resolution to 3 min. In addition, the analysis time is reduced to less than a minute.
Transmural recordings of gastrointestinal electrical activity using a spatially-dense microelectrode array
Authors: Nagahawatte Nipuni D , Paskaranandavadivel Niranchan , Angeli-Gordon Timothy R , Cheng Leo K , Avci Recep
Published: February 2021

Objective. High-resolution serosal recordings provide detailed information about the bioelectrical conduction patterns in the gastrointestinal (GI) tract. However, equivalent knowledge about the electrical activity through the GI tract wall remains largely unknown. This study aims to capture and quantify the bioelectrical activity across the wall of the GI tract. Approach. A needle-based microelectrode array was used to measure the bioelectrical activity across the GI wall in vivo. Quantitative and qualitative evaluations of transmural slow wave characteristics were carried out in comparison to the serosal slow wave features, through which the period, amplitude, and SNR metrics were quantified and statistically compared. Main results. Identical periods of 4.7 ± 0.3 s with amplitudes of 0.17 ± 0.04 mV versus 0.31 ± 0.1 mV and signal to noise ratios of 5.5 ± 1.3 dB versus 14.4 ± 1.1 dB were observed for transmural and serosal layers, respectively. Four different slow wave morphologies were observed across the transmural layers of the GI wall. Similar amplitudes were observed for all morphology types, and Type 1 and Type 2 were of the highest prevalence, dominating the outer and inner layers. Type 2 was exclusive to the middle layer while Type 4 was primarily observed in the middle layer as well. Significance. This study demonstrates the validity of new methodologies for measuring transmural slow wave activation in the GI wall and can now be applied to investigate the source and origin of GI dysrhythmias leading to dysmotility, and to validate novel therapeutics for GI health and disease.
Frustrated magnetism of the triangular-lattice antiferromagnets α-CrOOH and α-CrOOD
Authors: Liu Jiabin , Liu Benqiong , Yuan Long , Li Boqiang , Xie Lei , Chen Xiping , Zhang Hongxia , Xu Daye , Tong Wei , Wang Jinchen , Li Yuesheng
Published: February 2021

We report a comprehensive investigation of the frustrated magnetism on the delafossite oxides, α-CrOOH and α-CrOOD, which experimentally realize the S = 3/2 nearly-Heisenberg antiferromagnetic (J
1 > 0) model on a triangular lattice with weak single-ion anisotropy (D). The electron spin resonance (ESR), neutron scattering, and specific heat (C
m) measurements on both α-CrOOH and α-CrOOD consistently indicate that the long-range 120° Néel order is significantly suppressed and both systems are in the vicinity of a spin-liquid phase with C
m ∼ T
2 at low temperatures. The strength of D is quantitatively determined from fitting the high-temperature ESR linewidth and magnetic susceptibility, and its minus sign (D < 0, easy-axis type) is suggested by the low-energy (E ⩽ 2 meV) spin excitations. This easy-axis anisotropy (D/J
1 ∼ −5%) competes with the 120° Néel order and thus enhances the quantum spin fluctuations at low temperatures.
Classification of 13 original rock samples by laser induced breakdown spectroscopy
Authors: Wang Chong , Wang Jing , Wang Jing , Du Huan , Wang Jinghua
Published: February 2021

Rock classification plays a very important role in geological research. In order to simulate rock classification under unmanned conditions, we selected 13 kinds of rock samples and obtained their classification from spectral information, without any pretreatment, by using laser-induced breakdown spectroscopy (LIBS). Firstly, we collected all the characteristic regions of each rock and used principal component analysis to reduce the dimension of each LIBS spectral signal, to improve the accuracy and speed of the classification algorithm. Secondly, three classification algorithms were used to classify dimension-reduced spectral data, namely linear discriminant analysis, random forest classification and support vector machine (SVM). At the same time, the classification results were evaluated by confusion matrix. The final average classification accuracy was 27%, 91% and 100%, respectively, showing that the SVM algorithm can be applied to the LIBS classification of rocks.
Tunable mode-locked fiber laser to generate ultrashort cylindrical vector beams
Authors: Zhang Tao , Hu Haifeng , Chen Jian , Zhan Qiwen
Published: February 2021

In this work, we report a tunable mode-locked fiber laser that can produce ultrashort laser pulses with polarization vortex. Two vortex wave plates (VWPs) are employed in this laser to convert the intracavity beam states between the Gaussian mode and cylindrical vector mode. By adjusting the angle between a diffraction grating and the light beam in the cavity, the center wavelength of the mode-locked pulse can be tuned from 1015 nm to 1030 nm. Mode-locked pulses with full width at half maximum of 1.51 ps with the maximum single pulse energy of 2.2 nJ have been obtained. Through synchronously adjusting the orientations of the two VWPs, the output mode can be switched between radial polarization and azimuthal polarization states. The output vortex pulse from the tunable mode-locked fiber laser may find many applications such as optical manipulation, imaging and laser micro-machining.
Characterizations of Completely Nnc
(Weakly Nnc
)-irresolute Functions via Nnce-open Sets
Authors: Vadivel A. , Navuluri Mohanarao , Thangaraja P.
Published: February 2021

The purpose of present this paper is to introduce and investigate two new classes of Nnc
-irresolute functions called completely Nnce-irresolute functions and completely weakly Nnce-irresolute functions in topological spaces by using the concept of Nnce-open sets. Several new characterizations and interesting properties concerning completely Nnce-irresolute functions and completely weakly Nnce-irresolute functions are obtained. Furthermore the relationships between these functions and some other well-known types of functions are also given.
Predicting heart failure using data mining with Rough set theory and Fuzzy Petri Net
Authors: Meher Taj S. , Sudha M. , Kumaravel A.
Published: February 2021

The Rough Set Theory (RST) is a method that has proven its efficiency and simplicity in machine learning and successfully developing now a day’s vastly and rapidly. Fuzzy Petri nets (FPNs) are a potential modelling technique, which is used for knowledge representation and reasoning of rule-based expert systems. Though, RST has the efficiency in dimension reduction, it has to be proved with evidence by creating model using FPN with rule based reasoning. In this paper the induction of decision rules by RST executed with Fuzzy Petri Nets (FPN) is analyzed in the sense how it performed better than other data mining classifiers. The rule-based classifiers like jrip, part R, zero R are used for the comparison purposes. The knowledge captured from the rules through the best reduct has performed with the efficiency of RST and formulating rules by FPN. This paper experiments the Heart failure data to investigate the decision making from the rules generated by LERS system of RST with the approach of FPN. The heart failures during the follow up period of the patents are predicted with the pattern recognized form the data using the above process and the best evaluators are found during the experiments were pictured.
Zagreb Indices of Edge Based on IB Product
Authors: Pattabiraman K. , Suganya T.
Published: February 2021

A topological index is a numerical representation of a chemical structure which correlates certain physico-chemical characteristic of chemical compounds besides its numerical values. Zagreb index is the first degree based topological index which is proposed by Gutman et al.[10]in 1972. In this paper, we obtain the exact formulae for the Zagreb indices of new types and its coindices of two new types of graph operation based on indhu-bala product which is defined by Indulal and Balakrishnan [14] in 2016.
Agricultural Farming Survey Using Iot
Authors: Shali Mrs. A. , Sangeerani Devi Mrs. A. , Kavitha Mrs. D.
Published: February 2021

Web of things (IoT) is a promising innovation which gives productive and solid arrangements towards the modernization of a few spaces. IoT based arrangements are being created to naturally keep up and screen rural homesteads with negligible human association. The article presents numerous parts of advancements associated with the space of IoT in farming. It clarifies the significant segments of IoT based keen cultivating. A thorough conversation on network advances utilized in IoT based horticulture has been introduced, that includes network design and layers, network geographies utilized, and conventions. Moreover, the association of IoT based farming frameworks with pertinent advances including distributed computing, huge information stockpiling and examination has likewise been introduced. Likewise, security issues in IoT farming have been featured. A rundown of advanced mobile phone based and sensor based applications created for various parts of ranch the executives has likewise been introduced. Finally, the guidelines and approaches made by a few nations to normalize IoT based agribusiness have been introduced alongside barely any accessible examples of overcoming adversity.
On Nnc Zα
-Open Sets, NncA*L sets and Decompositions of Continuity
Authors: Balasubramaniyan K. , Gobikrishnan A. , Vadivel A.
Published: February 2021

In this paper, we introduce and study the notion of NncZα
-open sets and some properties of this class of sets are investigated. Also, we introduce the class of NncA*L sets via NncZα
-open sets. Further, by using these sets, a new decompositions of continuous functions are presented.
Homotopy Perturbation Method for Mathematical Modelling of Dengue Fever
Authors: Rekha S , Balaganesan P , Renuka J
Published: February 2021

Dengue fever is a viral mosquito-transmitted infection that has become a major international infection in recent years. The leading cause of disease and death in tropical and sub-tropical regions is a public health concern. Models from mathematical epidemiology, such as the classical SIR-model and its variants, are used to characterize the spread of Dengue in a given population. The mathematical modelling of Dengue Fever is formulated into a first-order nonlinear differential equation. Homotopy Perturbation Method approaches the analytical solution of the model (HPM), and also simulation results are identified. Finally, the analytical solutions, simulation results are compared, and satisfactory agreement is noted.
An example and experience of a mathematical modelling activity: the hip roof problem
Authors: Aziz T A , Makmuri
Published: February 2021

There is a growing body of literature that recognizes the importance of mathematical modelling which deal with real world problems in mathematics school. The present study set out to investigate potential uses of the Hip Roof problem to introduce a mathematical modelling activity to Indonesian students. This study was exploratory and interpretative in nature. The Hip Roof problem was originally developed by the researcher and was assigned to six groups of undergraduate students enrolled at one public university in Jakarta, Indonesia. Test administration and interview were conducted to describe their work. The works were analyzed descriptively. The result indicates that the problem is feasible with undergraduate students and the participants of the study appreciate the problem included in school mathematics to enhance their ability to implement mathematics in their real life. In solving the problem, the participants used their personal mathematical knowledge.
Mathematical study for an infectious disease with awareness-based SIS-M model
Authors: Latifah S , Aldila D , Giyarti W , Tasman H
Published: February 2021

In this article, we discuss a mathematical model of Susceptible-Infected-Susceptible-Media (SIS-M), which considers the level of human awareness on certain presence of disease. To construct the model, we divided the population based on their health status into susceptible individuals unaware and aware of the disease and the infectious individuals. The level of awareness included private awareness associated with direct contacts between unaware and aware populations, global awareness due to reported cases of infection, and regular awareness campaigns from media or policy makers. The dynamical behaviour of the model was analysed rigorously. The disease-free equilibrium point, the endemic equilibrium point, and the basic reproduction number were shown in this model analytically and numerically. We found that the disease-free equilibrium point was locally asymptotically stable if R
0 < 1, and unstable if R
0 > 1. From the sensitivity analysis of R
0, it was found that there was a minimum intensity for the awareness campaign so that the level of awareness manifested in the efforts of individuals to protect themselves from disease successfully eradicate the disease from the community. This result indicates that the efforts of individuals in protecting themselves can have a massive effect on the existence of the disease in the community.
The effect of Titanium Dioxide (TiO2) based metallic catalytic converter on the four-stroke motorcycle engine performance
Authors: Warju W , Drastiawati N S , Ariyanto S R , Nurtanto M
Published: February 2021

Many environmentally friendly automotive technologies have been developed to support the blue sky program. Among the several innovations, metallic catalytic converter (MCC) technology used in environmentally friendly motorcycle exhausts system with titanium dioxide (TiO2) is one of the innovations available to reduce the rise in air pollution due to exhaust emissions. In terms of reducing exhaust emissions, this technology certainly does not doubt its ability. But the problem is that not many researchers have examined the effect of the application of these technologies on engine performance. Thus, the high aim of this study is to analyze the effectiveness of the use of TiO2 based MCC on the four-stroke motorcycle engine performance. This type of experimental research uses a Yamaha Vixion motorcycle as an object of research. Engine performance testing is performed based on SAE J1349, namely Engine Power Test Code-Spark Ignition and Compression Ignition-Net Power Rating. The equipment used in this study is the inertia of the chassis dynamometer, digital tachometer, digital thermometer, humidity meter, and blower. The independent variables in this study are the standard muffler and modified muffler with TiO2 based MCC. The control variables are engine speed, engine oil temperature, ambient air temperature and humidity, and Pertamax fuel, while the dependent variable is engine performance (torque, power, and fuel consumption). Data analysis techniques using quantitative descriptive methods. This study found that the use of environmentally friendly motorcycle mufflers with TiO2 based MCC technology could increase the torque of the Yamaha Vixion motorcycles. In line with these results, vehicle power also increases, and fuel consumption is relatively economical if the TiO2-based MCC is compared to standard exhaust.
Guided inquiry blended learning tools (GI-BL) for school magnetic matter in junior high school to improve students’ scientific literacy
Authors: Gunawan G , Jufri A W , Nisrina N , Al-Idrus A , Ramdani A , Harjono A
Published: February 2021

This article discusses the use of learning tools in blended learning with a guided inquiry model in magnetic matter for junior high schools. Based on technological and educational developments, this learning tool was integrated with blended learning. The GI-BL (Guided Inquiry-Blended Learning) learning tools consist of magnetic textbooks, student worksheets, lesson plans and instruments for scientific literacy. The quasi-experimental method in this study used a pre-test post-test control group design. The GI-BL learning tools were developed and validated in terms of content as well as the instruments. Testing the effectiveness of this learning tool was carried out on 163 samples of junior high school students. GI-BL learning tools were developed to improve students’ scientific literacy, especially in solving problems related to magnetism-related phenomena. The data on the effectiveness of the GIBL learning tools were analysed using N-gain. Based on the results of the N-gain analysis, it appears that students who learn with GIBL learning tools can improve scientific literacy. The highest increase occurred in indicators describing scientific phenomena and in understanding the concept of Lorentz Force.
Analysis of Undergraduate Mathematic Students’ Understanding on Microscopic Representation of General Chemistry Lecture based on their Scientific Reasoning Ability
Authors: Asih F E , Mahdiannur M A , Aulia E V
Published: February 2021

Undergraduate mathematic students (UMS)’ scientific reasoning can affect their ability to do abstraction of microscopic phenomenon. Based on Piaget’s theory of cognitive development of UMS are in operational formal category, but this research was found that only 2 students of UMS whose have scientific reasoning as formal category. The aim of this research was analysing UMS’ achievement on understanding microscopic representation based on their scientific reasoning (concrete, transition, or formal category). Mixed method was used in this research which integrated quantitative data (test score) and qualitative data (analysing of understanding and scientific reasoning ability). The research funding that mean score test of UMS as formal reasoned (F) were the highest. UMS as transition reasoner and concrete reasoner were difficult to solve stoichiometry calculation which contains microscopic representation. Question about limiting reactant based on molecule representation, that 10,7% of UMS as concrete reasoner (C) answered there was no limiting reactant. The characteristic of concrete reasoner (C) is understanding based on observable object. They difficult to transform it as microscopic representation (number of molecules as reactant and product) which was written on net reaction as symbolic representation. They did not consider stoichiometric chemical equation by using representation of X as a variable in mathematical calculations. Another response that 14,3% of UMS as transition reasoner (T) determined limiting reactant by using logical mathematic equation of (mole=mass/molar mass) without considered coefficient in net reaction. In general chemistry learning, requires innovation to visualize microscopic aspects, so that UMS at concrete reasoning and transitions reasoning have the ability to interpret microscopic phenomena which is not observed by naked eyes.
Diabetes Mellitus Prediction System Using Hybrid KPCA-GA-SVM Feature Selection Techniques
Authors: Dinesh M G , Prabha D
Published: February 2021

Diabetes mellitus is a serious health issue in healthcare industry, which is a type of uncontrolled level of sugar. It is a chronic disease happened to the person who are having low insulin production and increase level of blood glucose because glucose is not properly utilized by body. In the medical field, predicting the correct diabetes is an important area that is under research to define a good predictive system to help the doctors to diagnose the disease. In the predictive system, feature selection plays on vital role to select the relevant feature for classification. There are several algorithms were applied on classification of diabetes data. In this proposed work, the features are transformed into high dimensional space before selection. So that the transformation of the features will give the better selection of attributes. With this effort, the proposed work implements the Kernel Principal Component Analysis for dimensionality reduction. KPCA will reduce the features space better than PCA. Once the features are transformed, the proposed work uses Genetic Algorithm to select the relevant and optimal features from the dataset. Then at the last Support Vector Machine is used as a classifier to classify the diabetes mellitus data. The proposed research on applying feature reduction before feature selection will reduce the irrelevant features that will improve the accuracy of the classification based on the selected relevant features. This proposed algorithm on diabetes mellitus data will compare with the existing algorithms to prove the effectives of the algorithm.
SEIR Model for COVID-19 Epidemic Using Delay Differential Equation
Authors: Devipriya R. , Dhamodharavadhani S. , Selvi. S
Published: February 2021

SEIR compartmental model for COVID-19 pandemic (SEIR_COVID) with exposed group is proposed and analysed in this paper. The steady state of this model is determined to control the pandemic. Zero Disease equilibrium and pandemic equilibrium are presented in SEIR_COVID model. In each case, the equilibrium prints are locally asymptotically stable under certain conditions. The Routh-Hurwitz criteria are used to study the stability of the equilibrium for COVID-19. This study showed that proposed SEIR_COVID model using Routh-Hurwitz criteria will be more effective for the COVID-19 pandemic prediction.
Data Visualization in Splunk and Tableau: A Case Study Demonstration
Authors: Balaji N , Karthik Pai B H , Bhat Bhaskar , Praveen Barmavatu
Published: February 2021

This article mainly deals with data analytics and visualization techniques. Data visualization techniques helps user to understand the data in a faster and easier way. Any data which is in the form of text is difficult to analyse in a short period of time, so using some colours or visualization techniques like bar chart, scattered plots will give a better understanding of any data in a glance. Using this technique we can analyse the data in a faster way and also represent a data in a batter way. Apart from data visualization, this internship focused on handling machine generated log data. Most of the time machines will generate some kind of log data in response to our actions, which is very difficult to understand. A study says that in the present world, machines will generate large amount of data, which are difficult to understand and analysing this data can be used for the betterment of the business or to invent new things. So to understand this machine generated data there is requirement of some experience with the process and also using of the tools, we can make the machine generated data divide into events and represent it in an easy and understandable manner.
Deep Learning Techniques in Tomato Plant – A Review
Authors: Mohana Saranya S , Rajalaxmi R R , Prabavathi R , Suganya T , Mohanapriya S , Tamilselvi T
Published: February 2021

Deep learning establishes an ongoing, modern technique for image processing with large potential and promising results. After proving its efficiency in various applications DL has also entered into the domain of agriculture. Here, we surveyed 38 research works that applied deep learning techniques to various research problems in tomato plant. We examine the areas of tomato plant research where deep learning is applied, data preprocessing techniques applied, transfer learning and augmentation techniques used. Studied dataset information like data sources used, number of images, classes and train test validation ratio applied. In addition, we study comparisons done on various deep learning architectures and discussed the outcome. The finding showed that DL techniques outperformed all other image processing techniques but DL performs mainly depends on the dataset used.
Cloud and Big Data: A Mutual Benefit for Organization Development
Authors: Abiodun Moses Kazeem , Awotunde Joseph Bamidele , Ogundokun Roseline Oluwaseun , Misra Sanjay , Adeniyi Emmanuel Abidemi , Arowolo Micheal Olaoluwa , Jaglan Vivek
Published: February 2021

The emergence of big data as a novel model gives unparalleled composition and value for an organization. The growth of big data is posing challenges in areas such as processing, analytics, sharing, and visualization. Interestingly, the cloud computing technology also emanated to specify important required infrastructure to tackle these disputes. Cloud computing is an effective interface that’s used to manipulate enormous-scale and complicated calculations. It eliminated the requirement to sustain lavish computing hardware, dedicated infrastructure, and software. Big data and cloud computing (BDCC) are considered to become the source of productivity that drives today’s electronic business transformations. Nonetheless, the ratio of the companies that adopt and use BDCC isn’t significant. There are also obstacles to facilitating BDCC’s market proliferation. Hence, this paper aims to recognize and emphasize the factors that affect the adoption and use of BDCC in technology-organization-environment (TOE) viewpoints in enterprises. Data were obtained from a sample of 100 companies to scientifically check our system. The understanding of BDCC’s potential advantages and technical capabilities are defined as the BDCC’s important elements. Consistency with current infrastructure, data reliability and functionality, and protection and privacy are highly rated within the sense of technologies. Managerial expertise and financial expenditure capacity for the application and use of BDCC and governmental assistance and regulation are defined as organizational and environmental considerations for acceptance and use, simultaneously.
Kannada word sense disambiguation using semantic relations
Authors: Manjunatha Kumar B.H. , Siddappa M
Published: February 2021

In this paper, a method is proposed to use the semantic relations of words to solve Word Sense Disambiguation (WSD) for the Kannada language. Semantic relationships like hypernymy and hyponymy are used in finding the overlaps between two concepts. Integrating the idea proposed in this paper with original Lesk’s technique improves the accuracy of the Lesk’s algorithm. Indo-WordNet is used as the lexical resource to gain the knowledge about the semantic relations of words and this knowledge is exploited to disambiguate the target word of the Kannada language. Since the approach used in this paper is unsupervised, so it can be scalable to other languages also.
Review on Crop Prediction Using Deep Learning Techniques
Authors: Dharani M K , Thamilselvan R , Natesan P , Kalaivaani PCD , Santhoshkumar S
Published: February 2021

Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion. Artificial intelligence gives a way to monitor the crop and to predict the yield in an automatized outcome. The study has been made on the deep learning and its hybrid techniques such as Artificial neural network, deep neural network and Recurrent neural network. It helped to identify how the technology of artificial intelligence helps to improve the crop yield. The research study clearly gives the idea and need of recurrent neural network and hybrid network in the field of agriculture. It also shows how it outperforms the other networks such as artificial neural network and convolutional neural network. The results were analyzed and the future perspectives were drawn with the obtained outcome.
Comparative analysis between theoretical and simulatory learning methods by data science methodology approach
Authors: Sharadkumar Patani Rucha , Shankar Narayan S
Published: February 2021

Data Science is the most trending interdisciplinary science that integrates the steps of data collection, preprocessing data, transforming data, storing data, data visualization, and hence extracting the insights from the data to serve stakeholder’s purposes. Python is commonly used and, along with being a versatile and open-source language, is a favorite tool in Data science studies. The vast libraries are being used to manipulate data and are very simple for even a beginner data scientist to understand. In the present work, we intend to apply the data science methodology to decision making and predictive analysis using the python programming language. We consider the problem of selecting the better mode of study concerning some of the impractical phenomena from physics for the exact understanding of the process. Data collection has been from an educational institute and the comparison has been made between theoretical learning and simulatory learning for selected topics from the vast fields like mechanics, thermodynamics, fluid dynamics, and radioactivity. The steps of data science methodology are germinated to achieve the insights into the data procured and the results are wangled concerning the teaching methodology that could be employed. In the present work, we undertake a comparative study between the theoretical and simulatory modes of teaching by exploring the modes individually through evaluating the responses imparted by a class of high school students. The analysis reported the more inclination of the student’s responses towards the simulatory methods when compared to the theoretical method of learning.