bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ambulance detection using image processing and neural networks
Authors: Agrawal K , Nigam M K , Bhattacharya S , Sumathi G 
Published: November 2021

Abstract
Ambulance Detection using Image Processing and Neural Network is a vehicle detection and tracking system, which recognizes the vehicle (i.e., Ambulance in this case) amidst the traffic congestion. Due to the fact from past few years, the range of vehicles usage of the road is growing each day that results in traffic congestion, for better management of this traffic this system is useful. Traffic Congestion, as mentioned above, can be observed at an ever-growing pace in countries like India and Thailand, where the roads’ width and length make it impossible to make a separate lane for the emergency vehicle (like that of ambulance); Hence making it hard for the vehicle to pass through the traffic at the earliest possible time. The Ambulance tracking system is activated at the mapped junctions and that program detects the ambulance coming close to it and turns the traffic light to Green for the next 15 seconds. Geocoding is the practice of transforming addresses (like a physical address) to location information (like longitude and latitude) that can be used to locate a label on a map or to mark a grid. They plan to provide ambulances with this software to make it easy to transform addresses into a programmable format for review and retrieval. This data is converted to a system that shows all the crossings it must pass to meet the endpoint.
Intelligent Healthcare System for Recuperation and Inoculation of Communicable Diseases
Authors: Jay Sarika , Prabhakar Rao B V A N S S 
Published: November 2021

Abstract
Human life gets interrupted a lot when a communicable disease starts in a society. People are alerted a lot on a different basis and eventually, it leads to difficulty for continuing their normal life. This paper is focused on a communicable disease, the COVID-19, which gave an outbreak for all world counties into a state where their fellow human had to stop their daily job and stay back at home. Common people find it difficult to get treatment when they are identified as positive and also most of them lack the awareness of post covid recovery. The idea of this work is to create a healthcare system that initiates right from the covid test, appropriate treatment according to severity, post-recovery, and vaccination awareness and jabs. These units will have less human interaction and they will be fully automated till in need of pure doctor consultation. The automated system is programmed to identify the infection, its severity and prescribe the treatment accordingly, and follow up recovery needs. On-side, automated vaccination system will allow direct walk-in, identify the customer and do the needful.
Web Based COVID Detection System using Deep Learning
Authors: Srivarshan S , Seshadri Prithvi , Kaarthikand E , Vijayalakshmi A 
Published: November 2021

Abstract
Currently COVID-19 is a disease that is ravaging the entire globe. Generally, people affected with COVID-19 will come down with a mild to moderate respiratory illness. Detecting COVID-19 has become a major concern in hospitals due to the sheer number of people who claim to have been suffering from the symptoms. This work presents a solution to this problem where the patient can determine whether he/she has COVID-19 or not. This research work tries to identify whether a person is infected with COVID or not by processing the X-Ray scan of the chest area using Deep Learning with the aid of a Neural Network. X-Ray images obtained from a GitHub repository have been used to train the model. Then the model can predict using the X-Ray image obtained from the user. Aweb application has been developed, to make this process seamless and efficient. The trained neural network is used to process the given image. This will be done in the back-end of the web application. If the person is infected with COVID, the application makes use of an advanced AI searching algorithm to find the most suitable physician based on the requirements of the user.
Evolution in diagnosis and detection of brain tumor – review
Authors: Sravanthi Peddinti A , Maloji Suman , Manepalli Kasiprasad 
Published: November 2021

AbstractDiagnosis of Brain tumor at an early stage has became an important topic of research in recent time. Detection of tumor at an early stage for primary treatment increases the patient’s survival rate. Processing of Magnetic resonance image (MRI) for an early tumor detection face the challenge of high processing overhead due to large volume of image input to the processing system. This result to large delay and decrease in system efficiency. Hence, the need of an enhanced detection system for accurate segmentation and representation for a faster and accurate processing has evolved in recent past. Development of new approaches based on improved learning and processing for brain tumor detection has been proposed in recent literatures. This paper outlines a brief review on the developments made in the area of MRI processing for an early diagnosis and detection of brain tumor for segmentation, representation and applying new machine learning (ML) methods in decision making. The learning ability and fine processing of Machine learning algorithms has shown an improvement in the current automation systems for faster and more accurate processing for brain tumor detection. The current trends in the automation of brain tumor detection, advantages, limitations and the future perspective of existing methods for computer aided diagnosis in brain tumor detection is outlined.
A Tree Based Machine Learning Approach for PTB Diagnostic Dataset
Authors: Premanand S , Narayanan Sathiya 
Published: November 2021

Abstract
The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.
Optimization of FSW Process Parameters to Yield Maximum Shear Fracture Load of AA2024 Aluminum Alloy Joints
Authors: Rajesh A , Bhaveshkumar C , Aswin L , Aravind Nachiappan S , Ashwin R 
Published: November 2021

Abstract
AA2024 Aluminum alloy has predominantly been used for making aircraft engine parts and frames. The normal welding process does not apply to join aluminum alloy. Because Al and its alloy have a low melting point and high thermal conductivity, which can easily lead to porosity and partially melting; as a result, Friction Stir Welding (FSW) has been employed to solve these issues. This work focused on the parameter’s optimization to the conceived maximum strength of AA2024 aluminum alloy. Four major parameters viz., tilt angle, shoulder diameter, welding speed, and rotational speed were selected. The formulation of empirical relationship was made using statistical tool design of experiment, and analysis of variance has been used to check the developed model’s adequacy. Furthermore, the response surface graphs were used to identify the maximum strength and its corresponding FSW parameters. The joint obtained the full power from the experimental results at a tilt angle of 1.5 deg., traverse speed of 15 mm/min, speed of tool 1100 rpm, and diameter of shoulder 24 mm.
Concept Design and Analysis of Self Sustainable Triboelectric Pacemaker
Authors: Ackshaya Varshini K S , Maanav Charan K.S. , Shyam Kumar M B 
Published: November 2021

Abstract
The heart is one of the most crucial organs for the functioning of the human body. Due to aging and various other ailments like cardiomyopathy and congestive heart failure, the functioning of the heart tends to drop or stop in serious conditions. In such conditions, a bio-medical device called the cardiac pacemaker is used. The pacemaker is a small device that will be placed in the dysfunctional heart that sends electrical impulses to the heart muscles whenever the functioning decreases or ceases. But the pacemaker existing in the market has a low battery life and has to be replaced every few years which is a painful process for the people using it. Therefore, to overcome this predicament in this study we have designed and developed a self-sustaining pacemaker that can generate its electricity from the pacing of the heart itself thereby increasing its battery life generously. This pacemaker works on the principle of tribe-electricity. The model of this pacemaker is designed using SolidWorks and the electrical circuit for the same is simulated using Simulink.
Estimation of fatigue life parameters of an Alumino Thermic weld on UIC60 rail joint using LEFM
Authors: Sen Prakash Kumar , Bhiwapurkar Mahesh , Harsha S P 
Published: November 2021

Abstract
At wheel track contact point, the high stress concentration, poor weld quality, and heterogeneity of weld material are the main factors that cause fatigue crack on any rail weld. Railway network agencies are concerned about the safety of the railway track when it comes to detecting and fixing weld faults to avoid vehicle derailment and loss of lives. This study analysed a numerical simulation of fatigue crack and its evolution under loaded service condition. A 3-D CAD wheel rail weld assembly model was built to study an AT welded joint under fatigue, and for stress concentration factor (SIF) calculation. The results are found by inserting a semi elliptical crack on the rail weld head surface with ANSYS, and then numerical simulation has been performed to get the different three modes of SIF at rail weld crack. The analysis findings data was recorded with critical fracture parameters of SIFs and its number of cycles to failure using LEFM technique and respective results have been plotted. With ANSYS the stress intensity on a crack will be resulted. By using numerical method, the critical crack size and number of cycle load with fatigue life of rail would be determined. The numbers of rail weld inspection per year has been determine by using the maximum number of cycle. The aim of this paper is to develop an effective inspection and maintenance frequency based on rolling contact surfaces crack propagation analyse. This will help to prevent the occurrence of rail failure by taking the required action at the right time, and extend the rail life expectancy, reduce the rail maintenance work and its cost.
Evaluation of nitrogen loading in the last 80 years in an urbanized Asian coastal catchment through the reconstruction of severe contamination period
Authors: Wang Kunyang , Onodera Shin-ichi , Saito Mitsuyo 
Published: November 2021

Abstract
Most semi-enclosed seas have experienced severe eutrophication owing to high nutrient loading from rivers during rapid population growth periods. In Japan, the coastal areas of some megacities (e.g. Tokyo and Osaka) experienced considerable economic growth during the 1960s–1970s. Therefore, determining the amount of nutrient loading during this period is essential to undertake measures for the conservation of coastal environments. However, determining the nutrient loading that occurred several decades ago is generally difficult owing to lacking water quality records. In this study, the nitrogen loading in the Yamato River catchment, an urbanized coastal catchment in Asia, for 80 years from the 1940s to the 2010s is reconstructed using the Soil and Water Assessment Tool. We considered factors such as population growth, wastewater treatment plant (WWTP) construction, and changes in land and fertilizer usage in different urbanization stages. Results show that the total nitrogen loading in the catchment peaked in the 1970s at 6616 tons yr−1 owing to untreated wastewater discharge and rapid increase in population growth. By reducing 57% of the nitrogen loading in the 2010s from the catchment, WWTPs have been instrumental in improving the water environment. The decrease in and integration of agricultural land has reduced nitrogen loading attributed to nonpoint sources; however, this reduction was not obvious because of the high fertilizer usage before the 2000s. Overall, the findings of this study provide a comprehensive understanding of the impact of rapid urbanization in an Asian coastal catchment on nitrogen loading during the high economic growth period in the past. This study will be useful for the long-term assessment of nutrient loading in other.
Diffusion with stochastic resetting of interacting particles emerging from a model of population genetics
Authors: da Silva Telles Timóteo , Fragoso Marcelo Dutra 
Published: November 2021

Abstract
In this paper, we put forward a connection between diffusion with resetting and a certain extension of Ohta–Kimura model, inspired on what was carried out in Da Silva and Fragoso (2018 J. Phys. A: Math. Theor.
51 505002). The contribution is twofold: (1) we derive a new extension of Ohta–Kimura model, dubbed here new extended version of Ohta–Kimura ladder model (NOKM) which bears a strong liaison with the so-called jump-type Fleming–Viot process. The novelty here, when we compare with the classical Ohta–Kimura model, is that we now have an operator which allows simultaneous interaction among many individuals. It has to do with a generalized branching mechanism i.e. m individual types extinguish and one individual type splits into m copies. The system of evolution equations arising from NOKM can be seen as a system of n-dimensional Kolmogorov forward equations (or Fokker–Planck equations). The analysis requires an amenable armory of concepts and mathematical technique to analyze some relevant issues such as correlation, indistinguishability of individuals and stationarity; (2) nudged by the ideas brought to bear in Da Silva and Fragoso (2018 J. Phys. A: Math. Theor.
51 505002), we advance in this agenda here by making an initial incursion on the connection between diffusion with resetting and the NOKM. The connection which relies on the similarities between the models allows, in some cases, that relevant results obtained for one model can be translated to the other model framework by taking advantage of the technique used to derive a result in one of the models. Through the development of the population genetic model and its reinterpretation in terms of diffusion with stochastic resetting, we show an invariance property of the correlation between two interacting particles that reset at a time-inhomogeneous resetting rate. Pushing forward the ideas we obtain the stationary state of a new model for an n-particle system under an anisotropic diffusion with resetting. Although the results using this approach are of recent vintage, we believe that this avenue of research seems to be very encouraging.
Twisted kink dynamics in multiflavor chiral Gross–Neveu model
Authors: Thies Michael 
Published: November 2021

Abstract
The Gross–Neveu model with UL(N
f) × UR(N
f) chiral symmetry is reconsidered in the large N
c limit. The known analytical solution for the time dependent interaction of any number of twisted kinks and breathers is cast into a more revealing form. The (x, t)-dependent factors are isolated from constant coefficients and twist matrices. These latter generalize the twist phases of the single flavor model. The crucial tool is an identity for the inverse of a sum of two square matrices, derived from the known formula for the determinant of such a sum.
The inspection paradox in stochastic resetting
Authors: Pal Arnab , Kostinski Sarah , Reuveni Shlomi 
Published: November 2021

Abstract
The remaining travel time of a plane shortens with every minute that passes from its departure, and a flame diminishes a candle with every second it burns. Such everyday occurrences bias us to think that processes which have already begun will end before those which have just started. Yet, the inspection paradox teaches us that the converse can also happen when randomness is at play. The paradox comes from probability theory, where it is often illustrated by measuring how long passengers wait upon arriving at a bus stop at a random time. Interestingly, such passengers may on average wait longer than the mean time between bus arrivals—a counter-intuitive result, since one expects to wait less when coming some time after the previous bus departed. In this viewpoint, we review the inspection paradox and its origins. The insight gained is then used to explain why, and under which conditions, stochastic resetting expedites the completion of random processes. Importantly, this is done with elementary mathematical tools which help develop a probabilistic intuition for stochastic resetting and how it works. This viewpoint can thus be used as an accessible introduction to the subject.
Position-dependent mass Dirac equation and local Fermi velocity
Authors: Ghosh Rahul 
Published: November 2021

Abstract
We present a new approach to study the one-dimensional Dirac equation in the background of a position-dependent mass m. Taking the Fermi velocity v
f to be a local variable, we explore the resulting structure of the coupled equations and arrive at an interesting constraint of m turning out to be the inverse square of v
f. We address several solvable systems that include the free particle, shifted harmonic oscillator, Coulomb and nonpolynomial potentials. In particular, in the supersymmetric quantum mechanics context, the upper partner of the effective potential yields a new form for an inverse quadratic functional choice of the Fermi velocity.
Comparative evaluation of varieties of winter hard wheat by the weight of 1000 grains with different morphotype of the germ
Authors: Kazakova A S , Galayan A G , Samofalova N E , Yu Maiboroda S , Kuleshova L A , Tatyanchenko I S 
Published: November 2021

Abstract
Abstracts. The productivity of plants depends on the weight of 1000 grains (M1000), the density of the productive stalk, the number of grains per ear, cultivation conditions, etc. The aim of the research was to comparatively evaluate M1000 in seeds differing in the morphology of the embryonic part, in varieties of durum winter wheat bred by the Agrarian Scientific Center “Donskoy”. The weight of 1000 grains were determined according to the State Standard 10842-89. The most productive and frequently encountered are the seeds with the main morphotypes 2, 3, 4, and 5, and the seeds with minor morphotypes 1a, 1, 6, and 7 make up 5%. An average positive relationship with M1000 of the entire heap of seeds was shown only for seeds of 2 main morphotypes - embryo morphotypes 2 (r = 0.45) and embryo morphotypes 5 (r = 0.42). Indicator M1000 embryo morphotypes 3 is not associated with M1000 of the whole seed heap, and all other embryo morphotypes for this trait have medium and strong negative correlation. Consequently, obtaining a fully formed grain in the dry spring and summer period is ensured by the stability of the filling of seeds of the main morphotypes 2 and 5.
The indices of sorghum seed quality in dependence on ecological and geographical origin
Authors: Kovtunov V V , Kovtunova N A , Popov A S 
Published: November 2021

Abstract
The current paper presents the study results of protein percentage, content of lysine and starch in the collection samples of grain sorghum of various ecological and geographical origin. It was determined that the seed protein percentage in the grain sorghum samples varied from 9.0 to 13.5%. The highest values were identified in the samples from Russia (KiM, Krymbel, Krusta, Ros’), Ukraine (Pioner 412/Milovskoe 6, No. 13-13, Pioner 878/Genicheskoe brown 129) and China (KX 8, No. 26-14) with 13.0-13.5%. The lysine content in protein of the studied samples was 2.5-4.3%, while the value of the standard variety Zernogradskoe 88 was 3.17% with the standard deviation of 0.24%. The maximum content (3.8-4.3%) was identified in the samples Early Hegari (Peru), Line ExF3 IS12606 (Australia), DN-35f (Ukraine), CS-175 (Philippines), H.S.-21 (Romania), Sorghum k-9553 (Korea). The starch content in seed of the collection samples varied within 70.2-77.5%. The highest intragroup values were showed by the samples from Peru (73.9%), Australia (73.8%), France (73.0%), Senegal (73.4%).
Spring vetch Mega mixed crops productivity at different seeding rates
Authors: Matveenko K A , Goncharov A V , Mednov A V , Volpe A A , Kruk E S , Abramkina L P 
Published: November 2021

Abstract
Spring vetch is the most well-known annual legume forage crop. With the cultivation and application of various methods and terms of harvesting crops, not only for green fodder and hay, but also for haylage, silage, grain-growing, the requirements for the quality of the mown mass were reduced, but the need to create non-growing crops suitable for machine harvesting increased. In the gene pool of spring vetch, there are no forms with a strong, resistant to lodging stem, so the only way to solve the problem is to support the supporting culture. According to the results of research for two years, for the cultivation of vetch for green mass, the best option is vetch mixed with oats with a seeding rate of 1.2 million vetch, oats of 3 million germinating seeds per hectare. For grain cultivation, the best option is a mixture of vetch and oats with a seeding rate of 1.8 million and 3 million germinating seeds, respectively. For obtaining a grain crop for seed-growing purposes, the best option for two years of research is vetch mixed with spring wheat with a seeding rate of 1.8 and 3 million germinating seeds per hectare.
The system of mineral nutrition of tomatoes in the Astrakhan region
Authors: Yu Anishko M , Ovchinnikov A S , Vilkova Zh A 
Published: November 2021

Abstract
The article considers the characteristics of tomato vegetable culture when growing it in the open ground in the soil and climatic conditions of the Astrakhan region. The total demand of tomatoes for macronutrients – nitrogen, phosphorus and potassium in the context of the planned yield from 40 to 100 tons per hectare, taking into account the availability of mobile forms of nutrients in the soil, is shown. The average doses of the main fertilizer application during the preparation of the soil for tomatoes, the timing and doses of fertilizing tomatoes with nitrogen fertilizers when they are applied with fertigation during sprinkling and the fertigation program for drip irrigation of tomatoes for processing are presented.