bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Counterfeit Products using Blockchain
Authors: Wasnik Kunal , Sondawle Isha , Wani Rushikesh , Pulgam Namita
Published: May 2022

Supply chain management frequently faced issues such as service redundancy, poor coordination between several departments, and lack of standardization as a result of the lack of transparency. Product counterfeiting is something which is very common now-a-days and it’s almost impossible to detect a counterfeit product just by looking at it. Counterfeiters cause significant challenges for legitimate firms, yet far too many people have no idea of the entire amount of counterfeit items’ influence on brands. There are several methods devised in the past to get away with this problem of product counterfeiting. The most popular methods are using RFID tags, Artificial Intelligence, QR code based systems, etc. But each of them had few disadvantages such as the QR code can be copied from a genuine product and placed on a fake product, artificial intelligence uses CNN and machine learning which needs heavy computational power and so on. The idea of this project is to improve detection of fake products by tracking its supply chain history. This is achieved with Blockchain technology which ensures the identification and traceability of real products throughout the supply chain. Blockchain based system, makes everything decentralized that may be accessed by several parties at the same time. One of its main advantages is that the recorded data is difficult to change without the consent of all parties concerned which makes the data extremely secure and protect from all vulnerabilities. This paper presents system designed using blockchain technology for detection of counterfeit products.
Farming Assistance for Soil Fertility Improvement and Crop Prediction using XGBoost
Authors: Deshmukh Mangesh , Jaiswar Amitkumar , Joshi Omkar , Shedge Rajashree
Published: May 2022

In India most vital and widely practiced occupation is Agriculture and it plays a vital role in the development of our country. Soil properties, rainfall, temperature, humidity and soil pH are the factors on which agriculture is depended. In agriculture, the selection of the wrong crop may reduce crop production. Farmers should know which crops can be grown in their area. Machine Learning-based solutions are widely used in the agriculture sector. This proposed work is a recommendation system in which Machine Learning techniques are used to recommend best three crops based on soil and weather parameters. The top three crops are recommended because farmers may not have access to a particular crop if only one crop is recommended. Previous studies in this field have been done by using different Machine Learning algorithms such as Random Forest, KNN, Naïve Bayes, etc. In this proposed system XGBoost Machine Learning algorithm is used which gives better results than other algorithms. In addition, the system provides information about how to improve the soil for growing the desired crop and gives the weather forecast for next five days. As a result, this system will help farmers minimize their financial losses while also increasing crop productivity.
Music Feature Extraction And Recommendation Using CNN Algorithm
Authors: Jha Aniket , Gupta Sagar , Dubey Priyanshu , Chhabria Aditi
Published: May 2022

In this age of technological advancements, it has become considerably easier for an individual to access a variety of music from a significant number of sources. Today, there are a multitude of songs of varying diversity available to users. Therefore, it becomes difficult for users to manually discover new music that may suit their liking. Thus arises the need for a system that will help the music streaming applications to recommend new music to their users that will befit their music taste, based on some predetermined criteria. With the ever-expanding user and song database, the system must also be dynamic and its recommendations must be up-to-date and accurate. Therefore, there is a strong demand for a well-qualified music recommendation system. The proposed system focuses of technical features of audio. The main purpose of this systems is to classify songs in different genre using Deep Learning Algorithm. There are two main approaches for implementing these system, viz, Feature Extraction and Content Based Filtering.
Drowsiness Detection using EEG signals and Machine Learning Algorithms
Authors: Joshi Adinath , Kamble Atharva , Parate Akanksha , Parkar Siddhesh , Puri Digambar , Gaikwad Chandrakant
Published: May 2022

Drowsiness is described as a state of reduced consciousness and vigilance accompanied by a desire or want to sleep. Driver tiredness is frequently detected using wearable sensors that track vehicle movement and camera-based systems that track driver behavior. Many alternative EEG-based drowsiness detection systems are developed due to the potential of electroencephalogram (EEG) signals to observe human mood and the ease with which they may be obtained. This paper applies Deep learning architecture like Convolutional Neural networks (CNN) and algorithms for the classification of EEG data for Drowsiness Detection. The key measures of video-based approaches include the detection of physical features; nevertheless, problems such as brightness limitations and practical challenges such as driver attention limits its usefulness. The main measure of video-based methods is the degree of closure of the eyelids; however, its success is limited by constraints like as brightness restrictions and practical challenges such as driver distraction. We have extracted statistical features and trained using various classifiers like Logistic Regression, Naïve Bayes, SVM, and K Nearest Neighbours and compared the accuracy using a deep learning CNN model. Results demonstrate that CNN achieved an accuracy of 94.75% by delegating feature extraction on itself. Upon comparing existing state–of–the–art drowsiness detection systems, the testing results reveal a higher detection capability. The results show that the the suggested method can be used to develop a reliable EEG-based driving drowsiness detection system.
Automated Script Outline Generator using TextRank Algorithm
Authors: Zope Urvi , Thakur Gayatri , Karambelkar Siddhi , Mane Vanita
Published: May 2022

The process of creating a condensed type of document that retains significant information and thus the general meaning of the source text is known as text summarization. Automatic summarization is a popular method for quickly locating significant information in vast texts with minimal effort. In today’s era information is the integral part of knowledge and it is too noisy and not in the precise manner for that both abstractive and extractive summarization techniques are used. The proposed model combines the summarization process for both text and image input type, which makes it easier for user to generate the precise summary regardless of the input type. The proposed model uses Textrank algorithm for the summarization process. Recall oriented understudy of gisting evaluation (ROUGH) score is used by the model to evaluate the accuracy of generated output. The model gives high ROUGH score which assures the accuracy of the generated summary.
Body Posture Detection and Motion Tracking using AI for Medical Exercises and Recommendation System
Authors: Patil Anuj , Rao Darshan , Utturwar Kaustubh , Shelke Tejas , Sarda Ekta
Published: May 2022

Exercises are highly essential in our everyday lives, especially when patients are in the middle of a healing process and need to speed up their body's recuperation. Exercise has become more important in our lives as a result of this. They provide the cornerstone for improving human capacities and extending their lives. Artificial Intelligence and Image Processing can be utilized to improve and supplement the workout process without the need for professional supervision. A software-based motion tracker can keep track of all the exercises you've done and provide you feedback on your posture while you're working out. Through computing data and analysis, the exercise's beneficial efficiency will be increased. The MediaPipe framework could be utilized for this application; in this machine learning model, points are plotted at several joints of the human body posture, and movement is tracked, stored, and analyzed. This detailed analysis of the body tracking could be used in the implementation of an application that could keep a track of the medical exercise of a registered individual. The software could be further improvised in such a manner that the registered user could be mapped to an authentic verified doctor having the access to the diagnosis reports and exercise history of the mapped patient using databases.
Density Based Traffic Control System with Convolutional Neural Network
Authors: Saple Mrunali R. , Jadhav Tejaswini M. , Kode Saurabh Y. , Gargi
Published: May 2022

We present a real-time traffic flow controller structure that can keep traffic under control using image processing techniques. In this way, a camera is used in every section of the robot to take pictures of the traffic where traffic jams will appear. The number of vehicles in these images is designed using image processing tools. In the proposed image, green and red signals are represented using LEDs and the diminished green signal supervisor is signified by a specific presentation.
Autonomous Hospital Assistant Bot
Authors: Pinto Pratik , Varghese Arun , Talari Francis , Edward Shalin , Pabarekar Amruta
Published: May 2022

In times of outbreaks, pandemics and otherwise there is no system in place to aid the doctors and nurses in their routine work such as monitoring, interacting, and providing necessities to the patient. These tasks tend to overburden the hospital staff and also bring them in proximity to the affected. Thus, The Autonomous Hospital Assistant Bot will act as a middleman through which the hospital staff can remotely monitor, interact and provide necessities like food, water, and medicine to the patient. Thus, reducing burden and proximity risks for the hospital staff. It can also establish communication between the patient and doctor through the bot enabling two-way communication. Bot also has a camera via which doctors can check the patient’s health credentials as displayed on the devices connected to them. The addition of the system to the hospital ecosystem will revolutionize the stagnated hospital culture regarding hospital staff and their work & difficulties. The bot can help to cut down these interactions exponentially since it takes care of the three major reasons for the routine check-ups i.e. patient monitoring, interaction, and providing the necessities, thus reducing these physical, in-person interactions down to only the critical patients who need more attention.
Image Super Resolution using Enhanced Super Resolution Generative Adversarial Network
Authors: Sarode Raj , Varpe Samiksha , Kolte Omkar , Ragha Leena
Published: May 2022

Aside from enhancing the accuracy and speed of single picture modification utilizing fast and in-depth convolutional emotional networks, one significant challenge remains mostly commonly unaddressed, namely how do we recover soft texture details when we concentrate too much on exceptional improvement features? The resultant evaluations offer greater transmission ratings, but the high frequency data is non-existent and unsatisfactory mostly in sense that now it fails to meet the consistency anticipated in high resolution. The resulting ratings have higher signal-to-audio ratings, but the high frequency data is non-existent and unsatisfactory in the sense that it fails to match the consistency expected in high resolution. Introducing ESRGAN, an Advanced Optical Genetically Modified (GAN) network of high-resolution image (SR). To our knowledge, it is a framework capable of identifying immature real-world images up to 4x points rising. To achieve this, we propose a function of loss of vision that combines the loss of content with the loss of content (Mean Squared Error Loss). Controversial Loss has our solution for many uncooked pictures utilizing a discriminatory network which is taught to distinguish between high resolution images and realistic images. We have built a structure that contains several RRDB blocks (Residual in Residual Dense Block) outside the Batch Normalization layers. Our deep residual network can find realistic image texture in very low sample images. Additionally, we used techniques that included residual measurement and a small implementation to train a deeper model. We also introduced the relativistic GAN as a racist, who learns to judge whether one image is more realistic than another, directing the generator to return a detailed texture. In addition, we have improved vision loss by using features before activation, which provides greater security and thus restoring more precise light and texture.
Deep Learning based Facial Emotion Recognition
Authors: Badhe Swayam , Chaudhari Sangita
Published: May 2022

Human Facial expression is the mirror of human emotions playing very significant role in nonverbal communication. In many applications like human machine interface, medical diagnosis, AI based games, market research etc. needs facial expression recognition. Although it is very easy task for humans, its bit challenging for machines to detect and recognize correct emotions from series of human facial expressions. Since decade, many researchers have tried different image processing, machine learning and deep learning-based approaches to correctly identify human emotions. Some of them could able to identify emotions but with more complexity. WE have proposed CNN model with 4 convolution layer and 2 FC layers which is giving good accuracy over the existing models with less complexity. It is performing better for all classes except fear and disgust.
N-gram models for Text Generation in Hindi Language
Authors: Ghude Tejashree , Chauhan Roshni , Dahake Krushna , Bhosale Atharv , Ghorpade Tushar
Published: May 2022

Native language plays a vital role for communication. Hindi is preferred by most Indians and it is the fifth most spoken language in the world. Hence, to make User Experience more effective while interacting with Software Applications, we aim to build a Model using Natural Language Processing which takes a specific word as an input and predicts the subsequent words for completing the sentence.It will act as a tab- complete function in Hindi language. This will also pave a way as a use case for building chatbots, personal emails, applications which are content based like cooking recipes, blogs, etc. in Hindi Language.
Business Meeting Summary Generation Using NLP
Authors: Jha Aryan , Temkar Sameer , Hegde Preetam , Singhaniya Navin
Published: May 2022

The effort of providing a brief and fluent summary of a business meeting while preserving vital information content and overall meaning is known as business meeting summarizing.Initially, we convert recorded meetings, interviews, speeches, lectures, and other audio streams into text documents in the proposed work. After that, speaker text is divided into segments. Minutes of the Meeting are generated automatically using the proposed work. We also focused on using the Rev-AI Speech-to-Text API to better the voice to text conversion of a specific recorded audio file while making sure that the summarised text that has been converted from speech to text provides a specific and accurate meaning that is not only understandable to everyone but also covers all of the recorded file’s important points.
Robust Home Alone Security System Using PIR Sensor and Face Recognition
Authors: Saxena Harsha , Ragha Leena
Published: May 2022

CCTV-based video monitoring technology is one of the fastest growing security technologies markets. The existing video monitoring systems are, however, still not in a position to be used to prevent crime. For public safety purposes, large networks of cameras are increasingly deployed in public places like Residential Buildings, College Campus, offices, airports, railway stations, and shopping malls. Such systems are primarily dependent on human observers and are therefore limited over long periods by factors such as exhaustion and monitoring. In order to overcome this constraint, "intelligent" systems are required, which can highlight the critical data and remove normal conditions that are not a safety hazard. We propose a model utilizing machine learning techniques in order to build these smart systems. This research aims to create an application in real time, which is necessary for labs, places of work or homes where human detection and Recognition will be done for human safety
Regional language Speech Emotion Detection using Deep Neural Network
Authors: Padman Sweta , Magare Dhiraj
Published: May 2022

Speaking is the most basic and efficient mode of human contact. Emotions assist people in communicating and understanding others’ viewpoints by transmitting sentiments and providing feedback.The basic objective of speech emotion recognition is to enable computers to comprehend human emotional states such as happiness, fury, and disdain through voice cues. Extensive Effective Method Coefficients of Mel cepstral frequency have been proposed for this problem. The characteristics of Mel frequency ceptral coefficients(MFCC) and the audio based textual characteristics are extracted from the audio characteristics and the hybrid textural framework characteristics of the video are extracted. Voice emotion recognition is used in a variety of applications such as voice monitoring, online learning, clinical investigations, deception detection, entertainment, computer games, and call centres.
Bloch waves in high contrast electromagnetic crystals
Authors: Lipton Robert , Viator Robert , Bolaños Silvia Jiménez , Adili Abiti
Published: May 2022

Analytic representation formulas and power series are developed describing the band structure inside non-magnetic periodic photonic three-dimensional crystals made from high dielectric contrast inclusions. Central to this approach is the identification and utilization of a resonance spectrum for quasiperiodic source-free modes. These modes are used to represent solution operators associated with electromagnetic and acoustic waves inside periodic high contrast media. A convergent power series for the Bloch wave spectrum is recovered from the representation formulas. Explicit conditions on the contrast are found that provide lower bounds on the convergence radius. These conditions are sufficient for the separation of spectral branches of the dispersion relation for any fixed quasi-momentum.
Dielectric spectroscopy of poly(ethylene oxide)–carbon nanotube nanocomposites
Authors: Getangama Nuwansiri Nirosh , de Bruyn John R. , Hutter Jeffrey L.
Published: May 2022

The dielectric properties of poly(ethylene oxide)–multiwalled carbon nanotube (MWCNT) nanocomposites have been studied over a wide range of frequency (0.1–106 Hz) and temperature (180–300 K). Nanocomposites were prepared by both melt mixing and twin-screw extrusion, and the concentration of MWCNTs was varied from 0 to 5 wt. %. Both the real and imaginary parts of the complex permittivity increase with the increasing MWCNT concentration. We observe a percolation transition in the DC conductivity of the composites above a critical MWCNT concentration pc. The data from the twin-screw extruded samples give a very well-defined value of pc and a percolation exponent of 1.9 ± 0.2, in good agreement with theoretical predictions. In contrast, both the percolation threshold and the critical exponent were more poorly defined for the melt-mixed nanocomposites. This indicates that the conductive properties of these materials can strongly depend on the details of sample preparation. Our data suggest that the dc conductivity of the nanocomposites is due to the conduction along the nanotubes, coupled with thermally activated transport of electrons across thin polymer bridges, which separate the nanotubes. The frequency dependence of the dielectric spectrum was studied as a function of temperature and composition. The primary dielectric relaxation process is due to the motions of electric dipoles on the polymer backbone. At low MWCNT concentrations, the relaxation involves the entire polymer chains and is slowed substantially when a low concentration of MWCNT is added. At higher MWCNT concentrations, the relaxation becomes much faster. We attribute this to binding of the polymer chains to the nanotubes, which reduces the length of the chain segments contributing to the dielectric relaxation.
Multiplex network disintegration strategy inference based on deep network representation learning
Authors: Zeng Chengyi , Lu Lina , Liu Hongfu , Chen Jing , Zhou Zongtan
Published: May 2022

Multiplex networks have attracted more and more attention because they can model the coupling of network nodes between layers more accurately. The interaction of nodes between layers makes the attack effect on multiplex networks not simply a linear superposition of the attack effect on single-layer networks, and the disintegration of multiplex networks has become a research hotspot and difficult. Traditional multiplex network disintegration methods generally adopt approximate and heuristic strategies. However, these two methods have a number of drawbacks and fail to meet our requirements in terms of effectiveness and timeliness. In this paper, we develop a novel deep learning framework, called MINER (Multiplex network disintegration strategy Inference based on deep NEtwork Representation learning), which transforms the disintegration strategy inference of multiplex networks into the encoding and decoding process based on deep network representation learning. In the encoding process, the attention mechanism encodes the coupling relationship of corresponding nodes between layers, and reinforcement learning is adopted to evaluate the disintegration action in the decoding process. Experiments indicate that the trained MINER model can be directly transferred and applied to the disintegration of multiplex networks with different scales. We extend it to scenarios that consider node attack cost constraints and also achieve excellent performance. This framework provides a new way to understand and employ multiplex networks.
Thermoelectric model to study the cardiac action potential and arrhythmias
Authors: Djoumessi R. T. , Rafiroiu Dan-Viorel , Pelap F. B.
Published: May 2022

This paper proposes a new thermoelectric model to examine the behavior of the heart in cooling situations. A modified Karma model with temperature-dependence is exploited to describe the ion exchange dynamics at the mesoscopic scale while the propagation of the action potential is governed by a mono-domain equation at the macroscopic scale. In addition to perfusion and heat metabolism, we call the Penne equation coupled to the mono-domain equation by using the Joule effect to depict the temperature behavior in the system. Galerkin’s finite element method is utilized to start solving the partial differential equations governing the action potential and temperature propagations. The incomplete lower–upper decomposition and generalized minimal residual methods are solicited to solve the resulting nonlinear system. The cases of zero temperature and potential gradients are integrated through the scheme of Runge–Kutta, and the results obtained corroborate well with those of the literature. We analyze the contributions of the nonlinear coupling tensor and arterial temperature on the thermal and electrical responses of the system. The established results reveal that when the temperature in the medium augments, the duration of the action potential decreases and the Joule coupling tensor plays a crucial role in the propagation of the potential. Moreover, we show that temperature and action potential are in phase and that propagation of this potential generates thermal energy. Furthermore, we establish the existence of spiral waves in heart cells and show that the effect of global cooling helps to modulate or dampen these spiral waves, leading to control of the cardiac arrhythmia. This work also develops a technique to resolve conduction disorders and cancel them completely. It exhibits an increased added value to the use of hypothermia as therapy during cardiac arrest and makes it possible to anticipate and perhaps avoid this pathology.
Exploring leakage in dielectric films via automated experiments in scanning probe microscopy
Authors: Liu Yongtao , Fields Shelby S. , Mimura Takanori , Kelley Kyle P. , Trolier-McKinstry Susan , Ihlefeld Jon F. , Kalinin Sergei V.
Published: May 2022

Electronic conduction pathways in dielectric thin films are explored using automated experiments in scanning probe microscopy (SPM). Here, we use large field of view scanning to identify the position of localized conductive spots and develop an SPM workflow to probe their dynamic behavior at higher spatial resolution as a function of time, voltage, and scanning process in an automated fashion. Using this approach, we observe the variable behaviors of the conductive spots in a 20-nm-thick ferroelectric Hf0.54Zr0.48O2 film, where conductive spots disappear and reappear during continuous scanning. There are also fresh conductive spots that develop during scanning. The automated workflow is universal and can be integrated into a wide range of microscopy techniques, including SPM, electron microscopy, optical microscopy, and chemical imaging.
The accumulation of particles in ureteric stents is mediated by flow dynamics: Full-scale computational and experimental modeling of the occluded and unoccluded ureter
Authors: Mosayyebi Ali , Vijayakumar Aravinthan , Mosayebi Maryam , Lange Dirk , Somani Bhaskar K. , Manes Costantino , Carugo Dario
Published: May 2022

Ureteric stents are clinically deployed to restore urinary drainage in the presence of ureteric occlusions. They consist of a hollow tube with multiple side-holes that enhance urinary drainage. The stent surface is often subject to encrustation (induced by crystals-forming bacteria such as Proteus mirabilis) or particle accumulation, which may compromise stent's drainage performance. Limited research has, however, been conducted to evaluate the relationship between flow dynamics and accumulation of crystals in stents. Here, we employed a full-scale architecture of the urinary system to computationally investigate the flow performance of a ureteric stent and experimentally determine the level of particle accumulation over the stent surface. Particular attention was given to side-holes, as they play a pivotal role in enhancing urinary drainage. Results demonstrated that there exists an inverse correlation between wall shear stress (WSS) and crystal accumulation at side-holes. Specifically, side-holes with greater WSS levels were those characterized by inter-compartmental fluid exchange between the stent and ureter. These “active” side-holes were located either nearby ureteric obstructions or at regions characterized by a physiological constriction of the ureter. Results also revealed that the majority of side-holes (>60%) suffer from low WSS levels and are, thus, prone to crystals accumulation. Moreover, side-holes located toward the proximal region of the ureter presented lower WSS levels compared to more distal ones, thus suffering from greater particle accumulation. Overall, findings corroborate the role of WSS in modulating the localization and extent of particle accumulation in ureteric stents.
High-performance silicon photonic filters based on all-passive tenth-order adiabatic elliptical-microrings
Authors: Liu Dajian , He Jianghao , Xiang Yuluan , Xu Yang , Dai Daoxin
Published: May 2022

Ultra-compact silicon photonic filters with box-like responses and high extinction ratios are proposed and demonstrated by introducing tenth-order adiabatic elliptical-microrings (AEMs) with bent directional couplers. For the AEMs, the waveguides in the non-coupling regions are wide and have a minimal bending radius, while the waveguides in the coupling regions are narrowed and have a maximal bending radius. The present silicon photonic filters based on tenth-order AEMs show a free spectral range as large as 37 nm for the first time. Even fabricated with commercial complementary metal–oxide–semiconductor-compatible 248 nm deep-ultraviolet lithography processes, the present devices show high performances with excess losses as low as ∼1 dB and extinction ratios as high as >60 dB. More importantly, the present silicon photonic filters work very well all-passively without any thermal control for calibration.
Techno-economic assessment of application of particle-based concentrated solar thermal systems in Australian industry
Authors: Beath Andrew , Meybodi Mehdi Aghaei , Drewer Geoffrey
Published: May 2022

Australia has significant areas with high quality solar resources, but the requirement for large scale solar thermal plants to be financially competitive in the electricity market appears to have hindered uptake. Industrial use of heat provides an alternative route to market where the technology is not impacted by poor efficiencies in converting heat to electricity and an appropriate scale can be applied to the demands of a specific site. A re-examination of prior industrial energy use studies in Australia was used in combination with solar data and published data on industrial sites to identify three specific industrial sites for case studies. These sites were selected to cover applications in three industries with varying scale and temperature requirements. The primary solar technology selected utilizes a particle receiver on a tower with associated storage and heat exchanger for hot water/steam production or heating a gas reactor. The wide range of temperatures possible with this technology appears to be desirable for development of a general-purpose industrial heat system. Comparison with parabolic trough systems that are commercially available was conducted for cases where the required temperatures were appropriate. In all assessments, the optimized solar plant designs approach cost competitiveness with the estimated cost of natural gas purchase for the relevant locations and industrial scales. In smaller and lower temperature applications, parabolic trough systems are likely to be appropriate conventional choices, but the particle system exhibited a high degree of flexibility across multiple sites and applications, which is encouraging to future commercial application.
Predictor feedback models for stick balancing with delay mismatch and sensory dead zones
Authors: Nagy Dalma J. , Insperger Tamás
Published: May 2022

Human stick balancing is investigated in terms of reaction time delay and sensory dead zones for position and velocity perception using a special combination of delayed state feedback and mismatched predictor feedback as a control model. The corresponding mathematical model is a delay-differential equation with event-driven switching in the control action. Due to the sensory dead zones, initial conditions of the actual state cannot always be provided for an internal-model-based prediction, which indicates that (1) perfect prediction is not possible and (2) the delay in the switching condition cannot be compensated. The imperfection of the predictor is described by the delay mismatch, which is treated as a lumped parameter that creates a transition between perfect predictor feedback (zero delay mismatch) and delayed state feedback (mismatch equal to switching delay). The maximum admissible switching delay (critical delay) is determined numerically based on a practical stabilizability concept. This critical delay is compared to a realistic reference value of 230 ms in order to assess the possible regions of the threshold values for position and velocity perception. The ratio of the angular position and angular velocity for 44 successful balancing trials by 8 human subjects was used to validate the numerical results. Comparison of actual human stick balancing data and numerical simulations based on the mismatched predictor feedback model provided a plausible range of parameters: position detection threshold 1°, velocity detection threshold between 4.24 and 9.35°/s, and delay mismatch around 100–150 ms.
Ionic mobility driven by correlated van der Waals and electrostatic forces
Authors: Samanta Tuhin , Matyushov Dmitry V.
Published: May 2022

Classical theories of dielectric friction make two critical assumptions: (i) friction due to van der Waals (vdW) forces is described by hydrodynamic drag and is independent of the ionic charge and (ii) vdW and electrostatic forces are statistically independent. Both assumptions turn out to be incorrect when tested against simulations of anions and cations with varying charge magnitude dissolved in water. Both the vdW and electrostatic components of the force variance scale linearly with the ionic charge squared. The two components are strongly anticorrelated producing simple relations for the total force variance in terms of self-variances. The inverse diffusion constant scales linearly with the charge squared. Solvation asymmetry between cations and anions extends to linear transport coefficients.
Understanding the growth of high-aspect-ratio grains in granular L1-FePt thin-film magnetic media
Authors: Xu Chengchao , Zhou Bing , Du Tianxiang , Varaprasad B. S. D. Ch. S. , Laughlin David E. , Zhu Jian-Gang (Jimmy)
Published: May 2022

A systematic investigation has been performed to optimize the microstructure of L10-FePt–SiOx granular thin films as recording media for heat-assisted magnetic recording. The FePt–boron nitride (BN) nucleation layer, which is stable even at 700 °C, is used to control the grain sizes and microstructure during the high-temperature processing. The study finds that films of high-aspect-ratio FePt grains with well-formed silicon oxide (SiOx) grain boundaries require the grading of the deposition temperature during film growth as well as the grading of the silicon oxide concentration. Well-isolated columnar grains of L10-FePt with an average height greater than 11 nm and diameters less than 7 nm have been achieved. Transmission electron microscopy analysis of the microstructures of samples produced under a variety of non-optimal conditions is presented to show how the microstructure of the films depends on each of the sputtering parameters.
Nonlocal hydrodynamic model for gravity-driven transport in nanochannels
Authors: Paul Arghyadeep , Aluru N. R.
Published: May 2022

It has been established that Newton’s law of viscosity fails for fluids under strong confinement as the strain-rate varies significantly over molecular length-scales. We thereby investigate if a nonlocal shear stress accounting for the strain-rate of an adjoining region by a convolution relation with a nonlocal viscosity kernel can be employed to predict the gravity-driven isothermal flow of a Weeks–Chandler–Andersen fluid in a nanochannel. We estimate, using the local average density model, the fluid’s viscosity kernel from isotropic bulk systems of corresponding state points by the sinusoidal transverse force method. A continuum model is proposed to solve the nonlocal hydrodynamics whose solutions capture the key features and agree qualitatively with the results of non-equilibrium molecular dynamics simulations, with deviations observed mostly near the fluid–channel interface.
The effect of trip wire on transition of boundary layer on a cylinder
Authors: Chopra Gaurav , Mittal Sanjay
Published: May 2022

The effect of height of a trip and its location on the transition of boundary layer on a cylinder is studied using large eddy simulations for 2×103≤Re≤5×105. The Reynolds number, Re, is based on the free stream speed and diameter of the cylinder (D). Two modes of transition are observed: (a) natural, for a relatively small trip of height dT/D=0.25%, via formation of a laminar separation bubble (LSB) and (b) direct, for a large trip of height dT/D=1.0%, wherein the formation of LSB is bypassed and the trip disturbs the flow enough to cause separation of the boundary layer and its subsequent turbulent reattachment. Transition delays the final separation leading to a very significant reduction in drag, often referred to as drag crisis. The delay is more for natural as compared to direct transition. Consequently, the drag at the end of crisis is lower for natural transition. The 1.0% trip at 78° leads to a more delayed flow separation than one at 55° from the front stagnation point. The drag crisis takes place in two stages for a cylinder with trip. During each of the two stages, asymmetric transition on the two sides results in generation of circulation and lift force. The effect of trip is felt even by the non-trip side. The cylinder experiences a relatively large “reverse lift” during the second stage of drag crisis. While natural transition is accompanied by intermittency of LSB, direct transition is associated with intermittency in laminar vs turbulent attachment of the flow following its separation at the trip.
Rarefied gas mixtures with large species mass ratio: Outflow into vacuum
Authors: Bykov N. Y. , Zakharov V. V.
Published: May 2022

The theoretical study of the processes of the outflow of a binary gas mixture from a source into a vacuum through an orifice in an infinitely thin wall is presented. Two mixtures with a large species mass ratio K are considered: Au–Ne (K = 9.76) and Au–He (K = 49.21). The work continues the study of the flow of Ag–He mixture (K = 26.95) started in Bykov and Zakharov [“Binary gas mixture outflow into vacuum through an orifice,” Phys. Fluids 32, 067109 (2020)]. The results of the direct simulation Monte Carlo made it possible to propose approximations of the mass flow rates of the species and the mixture depending on the species mass ratio, the flow rarefaction degree, and the mole fraction of light species in the source. It is shown that with an increase in the parameter K, an increase in the dimensionless mass flow rate of the mixture referred to the corresponding free molecular value is observed. The maximum dimensionless flow rate corresponds to the near-continuum regime and exceeds the value obtained using the hydrodynamic approximation and the equivalent single gas approach. A variation of K also leads to changes in the spatial distributions of the dimensionless density and velocity of the mixture and some axial focusing of the flow. An increase in the species mass ratio for the case of a small initial mole fraction of the heavy species in the source for a flow regime close to the hydrodynamic one leads to an increase in acceleration and axial focusing of the heavy species.
Orbital contraction and covalent bonding
Authors: Bacskay George B.
Published: May 2022

According to Ruedenberg’s classic treatise on the theory of chemical bonding [K. Ruedenberg, Rev. Mod. Phys. 34, 326–376 (1962)], orbital contraction is an integral consequence of covalent bonding. While the concept is clear, its quantification by quantum chemical calculations is not straightforward, except for the simplest of molecules, such as H2+ and H2. This paper proposes a new, yet simple, approach to the problem, utilizing the modified atomic orbital (MAO) method of Ehrhardt and Ahlrichs [Theor. Chim. Acta 68, 231 (1985)]. Through the use of MAOs, which are an atom-centered minimal basis formed from the molecular and atomic density operators, the wave functions of the species of interest are re-expanded, allowing the computation of the kinetic energy (and any other expectation value) of free and bonded fragments. Thus, it is possible to quantify the intra- and interfragment changes in kinetic energy, i.e., the effects of contraction. Computations are reported for a number of diatomic molecules H2, Li2, B2, C2, N2, O2, F2, CO, P2, and Cl2 and the polyatomics CH3–CH3, CH3–SiH3, CH3–OH, and C2H5–C2H5 (where the single bonds between the heavy atoms are studied) as well as dimers of He, Ne, Ar, and the archetypal ionic molecule NaCl. In all cases, it is found that the formation of a covalent bond is accompanied by an increase in the intra-fragment kinetic energy, an indication of orbital contraction and/or deformation.
Efficient routing for spatial networks
Authors: Lin Hong , Xia Yongxiang , Liang Yuanyuan
Published: May 2022

In many complex networks, the main task is to transfer load from sources to destinations. Therefore, the network throughput is a very important indicator to measure the network performance. In order to improve the network throughput, researchers have proposed many effective routing strategies. Spatial networks, as a class of complex networks, exist widely in the real-world. However, the existing routing strategies in complex networks cannot achieve good results when applied in spatial networks. Therefore, in this paper, we propose a new degree-location (DL) routing strategy to improve the throughput of spatial networks. In this routing strategy, the load transmitted from sources to destinations will bypass the nodes with high degrees and the nodes located close to the center of region. Simulations on homogeneous and heterogeneous spatial networks show that the DL routing strategy proposed in this paper can effectively improve the throughput of the network. The result of this paper can help the routing design of spatial networks and may find applications in many real-world spatial networks to improve the transmission performance.
Atom typing using graph representation learning: How do models learn chemistry?
Authors: Zhang Jun
Published: May 2022

Atom typing is the first step for simulating molecules using a force field. Automatic atom typing for an arbitrary molecule is often realized by rule-based algorithms, which have to manually encode rules for all types defined in this force field. These are time-consuming and force field-specific. In this study, a method that is independent of a specific force field based on graph representation learning is established for automatic atom typing. The topology adaptive graph convolution network (TAGCN) is found to be an optimal model. The model does not need manual enumeration of rules but can learn the rules just through training using typed molecules prepared during the development of a force field. The test on the CHARMM general force field gives a typing correctness of 91%. A systematic error of typing by TAGCN is its inability of distinguishing types in rings or acyclic chains. It originates from the fundamental structure of graph neural networks and can be fixed in a trivial way. More importantly, analysis of the rationalization processes of these models using layer-wise relation propagation reveals how TAGCN encodes rules learned during training. Our model is found to be able to type using the local chemical environments, in a way highly in accordance with chemists’ intuition.
Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data
Authors: Prebble Jessica M. , Symonds V. Vaughan , Tate Jennifer A. , Meudt Heidi M.
Published: May 2022

A taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis L.; Boraginaceae) is presented here. Climate-edaphic niches are modelled and compared for five species in the pygmy group, namely, M. antarctica Hook.f., M. brevis de Lange & Barkla, M. drucei (L.B.Moore) de Lange & Barkla, M. pygmaea Colenso and M. glauca (G.Simpson & J.S.Thomson) de Lange & Barkla, and one unnamed putative taxon, M. “Volcanic Plateau”. In this case, niche-modelling data mostly do not aid species delimitation, but morphological and genetic data provide evidence for recognising the following three species within the group: M. brevis and M. glauca (both endemic to New Zealand), and an enlarged M. antarctica (native to New Zealand, Campbell Island and Chile). Myosotis antarctica is here circumscribed to include M. antarctica sens. strict., M. drucei and M. pygmaea. The following two allopatric subspecies of M. antarctica are recognised on the basis of minor morphological differences: subsp. antarctica (formerly M. antarctica from Campbell Island and Chile, M. drucei and M. “Volcanic Plateau”) and subsp. traillii Kirk (formerly known by New Zealand botanists as M. pygmaea Colenso, an illegitimate name). For all three species, which are considered Threatened or At Risk, most of their genetic variation is partitioned between rather than within populations, meaning that conserving as many populations as possible should be the priority to minimise risk of extinction.
Spontaneous magnetization of collisionless plasma
Authors: Zhou Muni , Zhdankin Vladimir , Kunz Matthew W. , Loureiro Nuno F. , Uzdensky Dmitri A.
Published: May 2022

Significance

Astronomical observations indicate that dynamically important magnetic fields are ubiquitous in the Universe, while their origin remains a profound mystery. This work provides a paradigm for understanding the origin of cosmic magnetism by taking into account the effects of the microphysics of collisionless plasmas on macroscopic astrophysical processes. We demonstrate that the first magnetic fields can be spontaneously generated in the Universe by generic motions of astrophysical turbulence through kinetic plasma physics, and cosmic plasmas are thereby ubiquitously magnetized. Our theoretical and numerical results set the stage for determining how these “seed” magnetic fields are further amplified by the turbulent dynamo (another central and long-standing question) and thus advance a fully self-consistent explanation of cosmic magnetogenesis.
Hyperpolarized [5-
13
C,4,4-
2
H
2
,5-
15
N]-L-glutamine provides a means of annotating in vivo metabolic utilization of glutamine
Authors: Eskandari Roozbeh , Kim Nathaniel , Mamakhanyan Arsen , Saoi Michelle , Zhang Guannan , Berishaj Marjan , Granlund Kristin L. , Poot Alex J. , Cross Justin , Thompson Craig B. , Keshari Kayvan R.
Published: May 2022

Significance

Glutamine is the most abundant amino acid in human plasma, although it is challenging to determine glutamine’s metabolic fate noninvasively. In this work, we utilize established chemical methods to develop a platform for imaging glutamine metabolism using hyperpolarized magnetic resonance imaging. Using this strategy, we are able to spatially measure glutaminolysis in vivo as well as develop a biomarker for the inhibition of glutaminase. Combining this biomarker with isotope tracing metabolomics connects this inhibition to reduced glutamine contribution to the tricarboxylic acid cycle. This provides an approach for future imaging of glutamine metabolism in humans.
ATP synthesis in an ancient ATP synthase at low driving forces
Authors: Litty Dennis , Müller Volker
Published: May 2022

Significance

The ATP synthases of many anaerobic archaea have an unusual motor subunit
c
that otherwise is only found in eukaryotic V
1
V
O
ATPases. The evolutionary switch from synthase to hydrolase is thought to be caused by a doubling of the rotor subunit
c
, followed by a loss of the ion binding site. By purification and reconstitution of an ATP synthase with a V-type
c
subunit, we have unequivocally demonstrated, against expectations, the capability of such an enzyme to synthesize ATP at physiological relevant driving forces of 90 to 150 mV. This is the long-awaited answer to an eminent question in microbial energetics and physiology, especially for life near the thermodynamic limit of ATP synthesis.