bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of osteoporosis from simple hip radiography using deep learning algorithm
Authors: Jang Ryoungwoo , Choi Jae Ho , Kim Namkug , Chang Jae Suk , Yoon Pil Whan , Kim Chul-Ho
Published: October 2021

Despite being the gold standard for diagnosis of osteoporosis, dual-energy X-ray absorptiometry (DXA) could not be widely used as a screening tool for osteoporosis. This study aimed to predict osteoporosis via simple hip radiography using deep learning algorithm. A total of 1001 datasets of proximal femur DXA with matched same-side cropped simple hip bone radiographic images of female patients aged ≥ 55 years were collected. Of these, 504 patients had osteoporosis (T-score ≤ − 2.5), and 497 patients did not have osteoporosis. The 1001 images were randomly divided into three sets: 800 images for the training, 100 images for the validation, and 101 images for the test. Based on VGG16 equipped with nonlocal neural network, we developed a deep neural network (DNN) model. We calculated the confusion matrix and evaluated the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We drew the receiver operating characteristic (ROC) curve. A gradient-based class activation map (Grad-CAM) overlapping the original image was also used to visualize the model performance. Additionally, we performed external validation using 117 datasets. Our final DNN model showed an overall accuracy of 81.2%, sensitivity of 91.1%, and specificity of 68.9%. The PPV was 78.5%, and the NPV was 86.1%. The area under the ROC curve value was 0.867, indicating a reasonable performance for screening osteoporosis by simple hip radiography. The external validation set confirmed a model performance with an overall accuracy of 71.8% and an AUC value of 0.700. All Grad-CAM results from both internal and external validation sets appropriately matched the proximal femur cortex and trabecular patterns of the radiographs. The DNN model could be considered as one of the useful screening tools for easy prediction of osteoporosis in the real-world clinical setting.
The OpenDeID corpus for patient de-identification
Authors: Jonnagaddala Jitendra , Chen Aipeng , Batongbacal Sean , Nekkantti Chandini
Published: October 2021

For research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals. The corpus was developed by two annotators under three different experimental settings. The quality of the annotations was evaluated for each setting. Specifically, we employed serial annotations, parallel annotations, and pre-annotations. Our results suggest that the pre-annotations approach is not reliable in terms of quality when compared to the serial annotations but can drastically reduce annotation time. The OpenDeID corpus comprises 2,100 pathology reports from 1,833 cancer patients with an average of 737.49 tokens and 7.35 protected health information entities annotated per report. The overall inter annotator agreement and deviation scores are 0.9464 and 0.9726, respectively. Realistic surrogates are also generated to make the corpus suitable for distribution to other researchers.
A neoteric dual-channel spectral sensor for wide-range pH detection based on variables in UV–vis peak and intensity
Authors: Hu Lei , Zhang Mengxin , Dong Junwei , Dong Ran , Yu Caili , Gong Shijie , Yan Zhengquan
Published: October 2021

A water-soluble 2′,4′,6′-trihydroxy phenylazo luminol dye (THPL) was developed for pH detection from 3.0 to 10.0 by the dual-channel spectral absorption intensity (Abs) or peak (λmax) changes in aqueous solution.
Decoration of titanium dioxide nanotubes with silver nanoparticles using the photochemical deposition method and their application as an electrocatalyst to determine tinidazole
Authors: Koohi Fereshteh , Zare Hamid R. , Shekari Zahra
Published: October 2021

Schematic of the process of fabricating an electrode of titanium nanotubes decorated with silver nanoparticles (AgNPs/TiO2NTs) and using it to measure tinidazole.
Detection of falsified clopidogrel in the presence of excipients using voltammetry
Authors: Shergill Ricoveer Singh , Kristova Petra , Patel Bhavik Anil
Published: October 2021

This manuscript highlights a voltammetric method for the determination of falsified clopidogrel using voltammetry without extensive sample preparation.
Hollow MoS2/Co nanopillars with boosted Li-ion diffusion rate and long-term cycling stability
Authors: Wang Xunlu , Ma Ruguang , Wang Minmin , Wang Jin , Sun Tongming , Hu Lanping , Zhu Jinli , Tang Yanfeng , Wang Jiacheng
Published: October 2021

Hollow MoS2/Co-0.1 nanopillars synthesized by sulfurizing CoMoO4 and subsequent acid etching exhibit accelerated diffusion kinetics, leading to enhanced lithium storage performance.
Ring-opening polymerization-induced self-assembly (ROPISA) of salicylic acid o-carboxyanhydride
Authors: Shi Qianqian , Chen Yibing , Yang Junjiao , Yang Jing
Published: October 2021

Reported here is the first polyester-based bioactive nanoparticles accessed via ring-opening polymerization-induced self-assembly (ROPISA) of salicylic acid o-carboxyanhydride (SAOCA) monomers. Superfast ROPISA of SAOCA was completed in 30 seconds to form worm-like morphology.
A selenophene-containing conjugated organic ligand for two-dimensional halide perovskites
Authors: Wei Zitang , Wang Kang , Zhao Wenchao , Gao Yao , Hu Qixuan , Chen Ke , Dou Letian
Published: October 2021

A selenophene-containing conjugated organic ligand was synthesized and incorporated into a Sn(ii)-based two-dimensional perovskite. FET and LED devices based on these 2D perovskite thin films exhibit high performance and enhanced operational stability.
Aggregation effects on photophysical properties of NBN-doped polycyclic aromatic hydrocarbons: a theoretical study
Authors: Zeng Yi , Yang Junfang , Zheng Xiaoyan
Published: October 2021

Two NBN-doped polycyclic aromatic hydrocarbons (PAHs) with similar chemical structures have significantly different photophysical properties.
The combined action of cations and anions of ionic liquids modulates the formation and stability of G-quadruplex DNA
Authors: Sarkar Sunipa , Singh Prashant Chandra
Published: October 2021

G-Quadruplex (Gq) formation and stabilization by tetramethylguanidinium based ionic liquids is dependent on combined action of cation and anion.
Electrocatalytic reduction of CO2 and CO to multi-carbon compounds over Cu-based catalysts
Authors: Ma Wenchao , He Xiaoyang , Wang Wei , Xie Shunji , Zhang Qinghong , Wang Ye
Published: October 2021

This tutorial review discusses the similarities and differences between the electrocatalytic reduction of CO2 and CO to C2+ olefins and oxygenates over Cu-based catalysts.
Synthesis of tetranuclear rhenium(i) tricarbonyl metallacycles
Authors: Karges Johannes , Seo Hyeonglim , Cohen Seth M.
Published: October 2021

A facile synthetic strategy for the preparation of Re(i) tricarbonyl metallacycles is reported.
Dual-emission 3D supramolecular framework hydrogel beads: highly selective detection of antibiotics and mechanism research
Authors: Zhu Ke , Fan Ruiqing , Zhang Jian , Jiang Xin , Jia Wenwen , Wang Bowen , Lu Haoyang , Wu Jingkun , Wang Ping , Yang Yulin
Published: October 2021

The microstructure of dual-emission 3D supramolecular framework hydrogel beads and fluorescence response mechanism toward antibiotics.
Micro-controlling of the grain boundaries in NiCo2O4/NiCo2S4 nanowires for enhanced charge storage
Authors: Cui Mengqi , Wang Zining , Wang Hui , Wang Xuyun , Ren Jianwei
Published: October 2021

A NiCo2O4/NiCo2S4 material with rich grain boundaries has been grown on a nickel foam matrix by a vapor deposition method, and not only shows excellent specific capacitance performance, but also has high cycling stability.
Nanoelicitors with prolonged retention and sustained release to produce beneficial compounds in wines
Authors: Parra-Torrejón Belén , Ramírez-Rodríguez Gloria B. , Giménez-Bañón Maria J. , Moreno-Olivares Juan D. , Paladines-Quezada Diego F. , Gil-Muñoz Rocío , Delgado-López José M.
Published: October 2021

We designed nanoparticles able to protect the elicitor on the leaves of grapevines over long periods of time. The foliar application of this nanoelicitor produced wines with high content of stilbenes at significantly reduced dosages.
A proof of concept for cooperation from the quinone groups adjacent to N sites during the metal-free oxidation of glycerol by nitrogen-rich graphene oxide
Authors: Dogra Ashima , Sharma Vinit , Barlocco Ilaria , Villa Alberto , Gupta Neeraj
Published: October 2021

A catalytic active site already present in an organic molecule was chemically tagged on the GO surface to prove the role of quinone groups adjacent to N sites in nitrogen-rich carbon materials for the metal-free oxidation of glycerol.
Monazite LaPO4:Eu3+ nanorods as strongly polarized nano-emitters
Authors: Wang Zijun , Kim Jeongmo , Magermans Lilian , Corbella Francesca , Florea Ileana , Larquet Eric , Kim Jongwook , Gacoin Thierry
Published: October 2021

We report a microwave synthesis of monazite LaPO4:Eu3+ nanorods with high colloidal stability and quantum efficiency. High degrees of polarization for multiple emission lines enable high-resolution orientation analysis.
Correlations between the ECD spectra and absolute configuration of bridged-ring lactones: revisiting Beecham's rule
Authors: Zhang Jun-Yao , Yang Bei-Bei , Yang Ya-Dong , Gao Fan , Liu Wen-Qiang , Li Li
Published: October 2021

We present the theoretical verification, revision and enrichment of Beecham's rule for bridged-ring lactones. This provides an alternative way to correlate experimental ECD data with stereochemistry in addition to quantum-chemical calculation.
Enhancing catalytic activity of pyridines via para-iminophosphorano substituents
Authors: Richard Nicholas A. , Charlton Grant D. , Dyker C. Adam
Published: October 2021

Pyridine organocatalysts featuring 4-iminophosphorano substituents (R3PN–) are easily accessible, predictably tunable, and can surpass the activity of amino-substituted derivatives in the acylation of tertiary alcohols.
C–H bond cleavage-enabled aerobic ring-opening reaction of in situ formed 2-aminobenzofuran-3(2H)-ones
Authors: Wang Yingwei , Yang Mingrong , Lao Chichou , Jiang Zhihong
Published: October 2021

Based on α-C–H hydroxylation of carbonyl compounds, we realized the rare C–H bond cleavage-mediated ring-opening conversion of semicyclic N,O-acetals which generally occurs through a C–O bond cleavage.
Reversible cyclic-linear topological transformation using a long-range rotaxane switch
Authors: Aoki Daisuke , Aibara Gota , Takata Toshikazu
Published: October 2021

A reversible linear-cyclic topological transformation of polymers facilitated by a long-range rotaxane switch.
Nickel-catalyzed electrochemical reductive relay cross-coupling of alkyl halides with alkyl carboxylic acids
Authors: Jiao Ke-Jin , Ma Cong , Liu Dong , Qiu Hui , Cheng Bin , Mei Tian-Sheng
Published: October 2021

A highly regioselective Ni-catalyzed electrochemical (undivided cell) reductive relay cross-coupling between alkyl carboxylic acids and alkyl bromides has been developed.
Synthesis of N-indolated amino acids or peptides from 2-alkynylanilines via a dearomatization process
Authors: Qiu Weilian , Wang Weiyi , Liu Yin , Fan Renhua
Published: October 2021

A process for the rapid synthesis of N-indolated amino acids or peptides from readily available 2-alkynylanilines via dearomatization was reported.
Retraction: A rapid and novel method for the synthesis of 5-substituted 1H-tetrazole catalyzed by exceptional reusable monodisperse Pt NPs@AC under the microwave irradiation
Authors: Kaya Muharrem , Esirden İbrahim
Published: October 2021

Retraction for ‘A rapid and novel method for the synthesis of 5-substituted 1H-tetrazole catalyzed by exceptional reusable monodisperse Pt NPs@AC under the microwave irradiation’ by Esma Erken et al., RSC Adv., 2015, 5, 68558–68564, DOI: 10.1039/C5RA11426H.
Silacyclization through palladium-catalyzed intermolecular silicon-based C(sp2)–C(sp3) cross-coupling
Authors: Qin Ying , Li Lianghui , Liang Jin-Yuan , Li Kailong , Zhao Dongbing
Published: October 2021

Herein, we develop the first silacyclization between 2-halophenols and SCBs, which allows the modular assembly of sila-benzo[b]oxepines with good functional group tolerance and can be applied for the late-stage modification of biologically active molecules.
The molecular sieving mechanism of a polysulfide-blocking metal–organic framework separator for lithium–sulfur batteries
Authors: Jeon Taegon , Jung Sung Chul
Published: October 2021

This study is the first to elucidate the molecular sieving process of the MOF separator for Li−S batteries.
A multi‐agent reinforcement learning anti‐jamming method with partially overlapping channels
Authors: Zhang Yunpeng , Jia Luliang , Qi Nan , Xu Yifan , Chen Xueqiang
Published: October 2021

This paper investigates the problem of multi‐user anti‐jamming channel access with partially overlapping channels (POC). Compared with traditional anti‐jamming systems that use non‐overlapping channels, POC improve the spectral efficiency. However, the partial overlap of channels also brings more serious interference. For this, physical distance and channel separation on the interference intensity under partially overlapping channels are first considered and the malicious jamming and interference among users are formulated as a hierarchical binary model. Secondly, to cope with multi‐user decisions under dynamic jamming conditions, the Markov game framework is adopted to analyse the problem. Thirdly, a multi‐user collaborative anti‐jamming channel selection algorithm based on reinforcement learning is proposed as well as the optimal anti‐jamming strategy can be obtained. Finally, the simulation results validate that the proposed algorithm helps users cope with jamming and eliminate mutual interference. Compared with the non‐overlapping channel access scheme, the POC access scheme achieves higher network throughput.
Fault ride‐through strategy for hybrid HVDC with cascaded multi‐infeed MMC inverters
Authors: Zhao Jingbo , Wang Xiangfei , Li Baohong , Jiang Qin , Liu Tianqi
Published: October 2021

The hybrid HVDC with cascaded multi‐infeed MMC inverters suffers from the fragile characteristic of its MMC inverters and is easy to be over current and over voltage. To solve such a problem, this letter proposes a fault ride‐through strategy based on the fault current limiter with a novel topology. With the proposed limiter, the hybrid HVDC system can ride through both the DC side pole‐to‐ground fault and the commutation failure caused by the AC side fault. When the commutation failure happens, the current‐limiting resistor of the fault current limiter will be put on to decrease the current into the safety boundary. When the DC side pole‐to‐ground fault happens, the MMC capacitor discharge path will be cut off automatically by the topology of the fault current limiter. The simulations finally validate the analysis.
A new image processing strategy for surface crack identification in building structures under non‐uniform illumination
Authors: Parrany Ahmad Mahdian , Mirzaei Mohsen
Published: October 2021

Crack detection in building structures is an important approach to evaluating the safety of these structures. However, in some cases under special circumstances, it is very difficult to identify all features of cracks by visual inspection. To tackle this problem and offer a higher potential for practical implementations, developing an automatic crack detection method seems essential. This paper establishes a novel algorithm for surface crack detection in building walls based on image processing techniques to enhance crack visibility. One of the most prevalent issues in surface crack detection from digital crack images is non‐uniform illumination of background which makes narrow and tiny cracks indistinguishable. In this regard, an integrated approach, combining adaptive image threshold using local first‐order statistics, contrast‐limited adaptive histogram equalization, and noise filtering using non‐linear diffusion filtering, is proposed to extract the whole skeleton of the crack from digital crack images with non‐uniform illumination. Several realistic crack images, suffering from noise and uneven lighting of background, are processed through the proposed algorithm to demonstrate the robustness and high accuracy of the method.
Multiplicity of Galactic luminous blue variable stars
Authors: Mahy L. , Lanthermann C. , Hutsemékers D. , Kluska J. , Lobel A. , Manick R. , Miszalski B. , Reggiani M. , Sana H. , Gosset E.
Published: October 2021

Context.Luminous blue variables (LBVs) are characterised by strong photometric and spectroscopic variability. They are thought to be in a transitory phase between O-type stars on the main sequence and the Wolf-Rayet stage. Recent studies also evoked the possibility that they might be formed through binary interaction. Only a few are known in binary systems so far, but their multiplicity fraction is still uncertain.

Aims.We derive the binary fraction of the Galactic LBV population. We combine multi-epoch spectroscopy and long-baseline interferometry to probe separations from 0.1 to 120 mas around confirmed and candidate LBVs.

Methods.We used a cross-correlation technique to measure the radial velocities of these objects. We identified spectroscopic binaries through significant radial velocity variability with an amplitude larger than 35 km s−1. We also investigated the observational biases to take them into account when we established the intrinsic binary fraction. We usedCANDIDto detect interferometric companions, derive their flux fractions, and their positions on the sky.

Results.From the multi-epoch spectroscopy, we derive an observed spectroscopic binary fraction of 26−10+16%. Considering period and mass ratio ranges from log(Porb) = 0 − 3 (i.e. from 1 to 1000 days),q = 0.1 − 1.0, and a representative set of orbital parameter distributions, we find a bias-corrected binary fraction of 62−24+38%. Based on data of the interferometric campaign, we detect a binary fraction of 70 ± 9% at projected separations between 1 and 120 mas. Based on the derived primary diameters and considering the distances of these objects, we measure for the first time the exact radii of Galactic LBVs to be between 100 and 650 R⊙. This means that it is unlikely that short-period systems are included among LBV-like stars.

Conclusions.This analysis shows for the first time that the binary fraction in the Galactic LBV population is large. If they form through single-star evolution, their orbit must be large initially. If they form through a binary channel, the implication is that either massive stars in short binary systems must undergo a phase of fully non-conservative mass transfer to be able to sufficiently widen the orbit to form an LBV, or that LBVs form through merging in initially binary or triple systems. Interferometric follow-up would provide the distributions of orbital parameters at more advanced stages and would serve to quantitatively test the binary evolution in massive stars.
On the low ortho-to-para H2 ratio in star-forming filaments
Authors: Lupi Alessandro , Bovino Stefano , Grassi Tommaso
Published: October 2021

The formation of stars and planetary systems is a complex phenomenon that relies on the interplay of multiple physical processes. Nonetheless, it represents a crucial stage for our understanding of the Universe, and in particular of the conditions leading to the formation of key molecules (e.g. water) on comets and planets. Herschel observations demonstrated that stars form in gaseous filamentary structures in which the main constituent is molecular hydrogen (H2). Depending on its nuclear spin H2 can be found in two forms: ‘ortho’ with parallel spins and ‘para’ where the spins are anti-parallel. The relative ratio among these isomers, the ortho-to-para ratio (OPR), plays a crucial role in a variety of processes related to the thermodynamics of star-forming gas and to the fundamental chemistry affecting the deuteration of water in molecular clouds, commonly used to determine the origin of water in Solar System bodies. Here, for the first time, we assess the evolution of the OPR starting from the warm neutral medium by means of state-of-the-art 3D magnetohydrodynamic simulations of turbulent molecular clouds. Our results show that star-forming clouds exhibit a low OPR (≪0.1) already at moderate densities (∼1000 cm−3). We also constrain the cosmic-ray ionisation rate, finding that 10−16 s−1 is the lower limit required to explain the observations of diffuse clouds. Our results represent a step forward in the understanding of the star and planet formation processes providing a robust determination of the chemical initial conditions for both theoretical and observational studies.
A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases
Authors: Srınıvasa Rao B.
Published: October 2021

The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.
Optimization of Shell and Tube Condenser for Low Temperature Thermal Desalination Plant
Authors: Elakkiyadasan R , Manoj Kumar P , Subramanian M , Balaji N , Karthick M , Kaliappan S
Published: October 2021

In the current work, attempt for enhancing the heat exchanger of the shell and tube by analyzing the various parameters. The heat exchanger is a device used to transfer heat between at least two fluids. In the different kinds of heat exchangers utilized in various industries, shell and tube heat exchangers are presumably the most adaptable and widely heat exchangers utilized in most industrial areas. Based on the relationship between different parameters such as tube velocity, overall heat transfer coefficient, mass flow rate, and pumping power, analysis is carried out. Results show that the tube velocity increases the overall heat transfer coefficient, total pressure drop and mass flow rate of water, Pumping Power, up to the certain limit and starts to decrease. So that the parameters can be optimized by conducting the experiments based on different input parameters. The parameters which influence the optimal result are researched and recommended.
Independent Women Being Compassionate Towards Corporate Commitment
Authors: Rajeswari S. , Radha Seelaboyina , Kalyani K. Durga , Mamatha Botla
Published: October 2021

Increasing in opportunities for women has changed the world which fills workspace culture gap in several sectors. Empowering women is one of the most pressing criteria for economic and social needs of a nation. The study focuses on contributions of women in corporate sector and sustaining active engagement in their personal life. Women disruptions to employment, stimulating deeper thinking with targeted business actions and self-management brings the most possible change in society. The findings show that women have developed considerable effect on the academic front whereas ever-increasing work pressure is urging working women to spend less time for themselves. Women contributions in corporate sector is extremely important for prosperity and success of nations that make an incredible progress across the globe. The methods suggest that creating an envisioned process to improve gender parity in society by capitalizing human talents will make a better work place for women. This dearth of women in corporate boardrooms definitely steers the accountability. Results show that the presence of women in management positively with commitment and leadership affects significant untapped source of executive talent leading the way in decision and policy making.
A critical review on ultra high molecular weight polyethylene (UHMWPE) for prosthesis and implant functions
Authors: Singh Devendra Kumar , Verma Rajesh Kumar
Published: October 2021

Polymer composites benefit joint prostheses and implants in biomaterials due to their high strength, reliability, and elasticity modules. The addition of nanoparticles into the polymer-based matrix has effectively demonstrated up-grading wear resistance and implant strength improvement. Therefore, due to the elevated surface area and immense properties, considerable attention has been paid to research in integrating nanoparticles for a wide variety of functions. The UHMWPE is extensively used to develop prosthesis and orthopedic operations due to exceptional mechanical and biocompatible features. The various research studies revealed the fabrication of bio nanocomposites with the polymer matrix possesses superior biocompatibility and durability. This paper presents a critical review of UHMWPE for the latest advancement in polymeric implants by adding different nanoparticles. Another exciting aspect of the proposed work is the addition of different organic (carbon, polymeric) and inorganic (metallic and metal oxides) nanoparticles to develop bio-nano composites. An effort has been made to highlight the exceptional features of modified UHMWPE by supplementing nanofillers for biomedical functions.
Avr System Analysis and Simulation by Using Fopid and Parameters Variation Effects
Authors: Srikanth Choppadandi , Padhan Dola Gobinda , Kumar N. Kiran
Published: October 2021

The FOPID control units for an AVR system with a fractional filter are a unique fractional order. The main responsibility for controlling the reactive power and voltage level is an automated tensile regulator (AVR). PID controller, sensor, exciter, and stabiliser or amplifier are used for the module system. The system is designed according to state-space technology. The proposed controller must have seven independent parameters. A comparison of the published study with optimal adjusted AVR PID and FOPID controls also shows that the proposed controller is superior. The recommended controls derived from the bode analysis with their frequency response characteristics are shown. Finally, the resilience of the controller design is individually examined for both the parameter uncertainties of AVR system and outside storages introduced into AVR system. Given the overall results presented there are clear improvements to the performance of AVR system by a fractional filter in a proposed FOPID controller, and that the AVR system may successfully be applied to the suggested control.
ANN based LFC with Coordination strategies of DERs in Hybrid Isolated Micro-Grid Environment
Authors: Boyini Srikanth , Palakaluri Srividya Devi , Mudundi Rekha
Published: October 2021

This paper provides a load frequency control (LFC) of a micro grid with renewable energy resources (RES). The operation of micro grid with a low inertia system leds to disturbances in power system. The disturbances in frequency is more in micro grid than conventional power system. So there should be a fast recovery of changes in frequency with existing system and interconnected system (RES). Active power injection is the main scheme to control frequency of a system. The matlab simulink tells us that different active power injection system contribute for the fast control of grid frequency with PID controller. The use of ANN technology to this system the load frequency control can be illustrated in faster rate of its recovery. An ANN controller is investigated which handles the inputs collectively in each sector of the power system. Back-transmission time is normally used in the study for neural network education. The performance of the power system is simulated independently with a typically integrated conventional controller and ANN controller. A complete spectrum of small signals is introduced for RESs in the isolated microgrid and a correct role in frequency control studies is taken into account.
Sarcasm Discernment on Social Media Platform
Authors: Sagarika Namasani , Sreenija Reddy Bommadi , Varshitha Vanka , Geetanjali Kodavati , Ganapathi Raju N V , Kunaparaju Latha
Published: October 2021

Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from Huff Post. Sarcasm Detection on social media platform. The dataset is collected from two news websites, theonion.com and huffingtonpost.com. Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings. Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets. Unlike tweets that reply to other tweets, the news headlines obtained are self-contained.
A Novel Approach of Smart Water Flow Meter using IOT
Authors: Kranthi Madala , Sreenivas Velagapudi , Prabhakar K. , Ramesh G.
Published: October 2021

The point of this paper is on DESIGN OF SMART WATER FLOW METER which has gotten a conspicuous subject inside the present mechanical discourse. During this snappy paced lifestyles water providers and customers need to present any other water system which is steadily gainful and also faster digital water meters are adjusted test the degree of water used by private and commercial enterprise structures which are given water through an untamed office device. Thus via using this we are able to retain seeing at the usage of water by way of diverse clients. The essential target is to shape office logically in a position and direct which lessens the manual help, terminate affirm and time gifted response for the incredible water shape. This snappy paced existence water providers and shoppers wishes to introduce some other water gadget which is regularly successful and nearly snappier. Modernized water meters are adjusted examine the proportion of water used by private and business systems which are given water by way of an untamed water device. On this manner by using this we will continue disapproving of the usage of water via various customers. The fundamental purpose is to form water progressively reliable and direct which diminishes the manual help, take a seat back equipped course of motion for the dumbfounding water structure. Aside from created international locations, the water elements in others gift with a sensor that is labored electronically. Throughout this enterprise a node mcu based charging structure is given. That is frequently a structure which prompts a homogenous trouble free office what’s more, charging giving most notable agreement to customers and development in water use survey.
A Survey on Cardiovascular Prediction using Variant Machine learning
Authors: Chandrika L. , Madhavi K. , Sindhuja B. , Arshi M.
Published: October 2021

Prediction of a cardiovascular diseases has always a tedious challenge for doctors and medical practitioners. Most of the practitioners and hospital staff offers expensive medication, care and surgeries to treat the cardiovascular patients. At early-stage of prediction of heart-oriented problems will be giving a chance of survival by taking necessary precautions. Over the years there are different types of methodologies were proposed to predict the cardiovascular diseases one of the best methodologies is a Machine learning approach. These years many scientific advancements take place in the Artificial Intelligence, Machine learning, and Deep learning which gives an extra push up to help and implement the path in the field of medical image processing and medical data analysis. By using the enormous dataset from various medical experts used to help the researchers to predict the coronary problems prior to happening. Many researchers have tried and implemented different machine learning algorithms to automate the prediction analysis using the enormous number of datasets. There are numerous algorithms and procedures to predict the cardiovascular diseases and accessible to be specific Classification methods including Artificial Neural Networks (AI), Decision tree (DT), Support vector machine (SVM), Genetic algorithm (GA), Neural network (NN), Naive Bayes (NB) and Clustering algorithms like K-NN. A few examinations have been done for creating expectation models utilizing singular procedures and additionally concatenating at least two strategies. This paper gives a speedy and simple survey and knowledge of approachable prediction models using different researchers work from 2004 to 2019. The examination indicates the precision of individual experiments done by various researchers.
A Hybrid Framework for Heart Disease Prediction Using Machine Learning Algorithms
Authors: Chandrika L. , Madhavi K.
Published: October 2021

Cardiovascular Diseases (CVDs) are the primary cause for the sudden death in the world today from the past few years the disease has emerged greatly as a most unpredictable problem, not only in India the whole planet facing the criticality. So, there is a desperate need of valid, accurate and practical solution or application to diagnose the CVD problems in time for mandatory treatment. Predicting the CVD is a great challenge in the health care domain of clinical data analysis. Machine learning Algorithms (MLA) and Techniques has been vastly developed and proven to be effective and efficient in predicting the problems using the past data. Using these MLA techniques and taking the clinical dataset which provided by the healthcare industry. Different studies were takes place and tried only a small part into predicting CVD with ML Algorithms. In this thesis, we propose the different novel methodology which concentrates at finding appropriate features by using MLA techniques resulting at finding out the accurate model to predict CVD. In this prediction model we are trying to implement the models with different combinations of features and several known classification techniques such as Deep Learning, Random Forest, Generalised Linear Model, Naïve Bayes, Logistic Regression, Decision Tree, Gradient Boosted trees, Support Vector Machine, Vote and HRFLM and we have got an higher accuracy level and of 75.8%, 85.1%, 82.9%, 87.4%, 85%, 86.1%, 78.3%, 86.1%, 87.41%, and 88.4% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).
Detection of Fake Profiles on Twitter Using Hybrid SVM Algorithm
Authors: Kodati Sarangam , Pradeep Reddy Kumbala , Mekala Sreenivas , Srinivasa Murthy PL , Chandra Sekhar Reddy P
Published: October 2021

Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm.
Development of hybrid fibre reinforced self-compacting concrete as per Nan Su criteria
Authors: Rama Krishna Rao A , Ruben N , Reddy Srinivasa V , Sankeerth S V S
Published: October 2021

This paper studies the development of hybrid fibre reinforced self-compacting concrete as per Nan Su criteria. Results predicted that various packing factors adopted in the study are 1.12, 1.14, 1.16 and 1.18. Fine aggregate /Total aggregate ratios (s/a ratio) adopted in the study are 0.5, 0.53 and 0.57. The optimum combinations of packing factor and s/a ratio are found to be 1.12 & 0.53 and 1.14 & 0.57 for M30 grade SCC mixes because these optimum PF and s/a ratio combination gives comparatively better particle packing density in SCC mixes. Better particle packing density enhances the microstructure of SCC mix subsequently more strength and durability can be achieved. As PF increases powder content decreases and aggregate content increases requiring more paste to make the SCC mix workable. Less value PF will have high particle packing density yielding more strength due to improved microstructure of SCC mixes. At PF & s/a combinations of 1.12 & 0.53 and 1.14 & 0.57, the workability of SCC mixes is superior because of high paste volume and less aggregate content. Compressive, split-tensile and flexural of M30 grade SCC mixes made with optimum combinations of packing factor and s/a ratios are found to be high.
Studies on stress- strain behaviour of fibre reinforced self-compacting concrete in confined state
Authors: Jayasri Gorla , Prasad Raju V Siva , Srinivasa Reddy V , Mounika M
Published: October 2021

In the present study, the stress-stain behaviour of self-compacting concrete (SCC) and fibre reinforced self-compacting concrete (FRSCC) were taken up. The stress-strain behaviour was studied for the SCC and FRSCC mixes in unconfined and confined states. The confinement was given in the form of steel hoops in the cylinders, 3 hoops (0.8%), 4 hoops (1.1%), 5 hoops (1.3%) and 6 hoops (1.6%). The addition of fibres along with confinement of FRSCC with steel hoops enhanced the compressive strength, indicating further confinement effect in the FRSCC. It is observed that the addition of fibres is helpful in lower confinements only. Beyond 1.1% confinement, the addition of any type of fibres doesn’t show any effect on compressive strengths. From the stress-strain behaviour of all types of FRSCC, it is concluded that the ultimate load-carrying capacity and strains at peak stresses are more in SFRSCC and HFRSCC for mixes up to 1.1% confinement. The addition of fibres to SCC has increased the ductility in both confined and unconfined states
Effect of packing factor and fine aggregate/total aggregate ratio on the development of self-compacting concrete
Authors: Sankeerth S V S , Srinivasa Reddy V , Shrihari S , Rajashekar C
Published: October 2021

This paper presents the effect of packing factor and fine aggregate/total aggregate ratio on the performance of the self-compacting concrete (SCC). A higher PF value would imply a greater amount of the coarse and fine aggregates used, thus, decreasing the content of binders in SCC. In this paper M30 grade plain SCC mixes are developed with the different combinations of packing factors (PF) (varying from1.12 to 1.18) and fine to total aggregate ratios (s/a) (0.50 to 0.57) and found that the PF & s/a combinations of 1.12 & 0.53 and 1.14 & 0.57 are found to be optimum as these combinations gave maximum compressive strengths which can be attributed to high achievement of particle packing densities in SCC mixes. As PF increases powder content decreases and aggregate content increases requiring more paste to make the SCC mix workable. Less value PF will have high particle packing density yielding more strength due to improved microstructure of SCC mixes. At PF & s/a combinations of 1.12 & 0.53 and 1.14 & 0.57, the workability of SCC mixes is superior because of high paste volume and less aggregate content.
Flexural behaviour of hybrid fibre reinforced concrete beams made with various packing factors and fine to total aggregate ratio
Authors: Siva Prasad Raju V , Jayasri Gorla , Srinivasa Reddy V , Naga Sai Baba A
Published: October 2021

The objective of this study is to investigate the flexural behavior of M30 grade PSCC, GFRSCC, SFRSCC and HFRSCC beams made with PF=1.12 and s/a=0.53 and PF=1.14 and s/a=0.57 to understand the effect of copper slag as partial replacement of fine aggregate on its deflection characteristics and cracking behaviour. The yield and ultimate load taken by HFRSCC beams made with optimum PF and s/a ratios are higher than the conventional RCC beam elements. The deflections at centre at failure in HFRSCC beams made with optimum PF and s/a ratios were more than that of conventional beams. This shows improvement in ductility of HFRSCC beams. First crack formation was delayed in M30 grade HFRSCC beams due to dense micro structure with low pore fraction and reduced pore size due to which fatigue strength is increased which in turn increases the time taken for first crack occurrence and thereby increasing the load carrying capacity. The deflection at the mid span decreased in HFRSCC beams which shows that the flexural stiffness of the elements increases thereby reducing the structural member’s deformability, increasing strength and hence controlling deflection.
CNN Based Monocular Depth Estimation
Authors: Swaraja K. , Naga Siva Pavan K. , Suryakanth Reddy S. , Ajay K. , Uday Kiran Reddy P. , Kora Padmavathi , Meenakshi K. , Chaitanya Duggineni , Valiveti Himabindu
Published: October 2021

In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates. With the use of transfer learning, this research executes a convolutional neural network for generating a high-resolution depth map from a single RGB image. With a typical encoder-decoder architecture, when initializing the encoder, we use features extracted from high-performing pre-trained networks, as well as augmentation and training procedures that lead to more accurate outcomes. We demonstrate how, even with a very basic decoder, our approach can provide complete high-resolution depth maps. A wide number of deep learning approaches have recently been presented, and they have showed significant promise in dealing with the classical ill-posed issue. The studies are carried out using KITTI and NYU Depth v2, two widely utilized public datasets. We also examine the errors created by various models in order to expose the shortcomings of present approaches which accomplishes viable performance on KITTI besides NYU Depth v2.
Battery, Ultra-capacitor based Hybrid Energy Storage System (HESS) for EV applications with PI and Fuzzy logic controllers
Authors: Santhosh Jonnalagadda , Rajagiri Anil Kumar , Morri Prashanth
Published: October 2021

The extensive use of vehicles based on IC engines has contributed to serious effects on the environment and accelerated depletion of fossil fuel reserves which has led to a rapid increment in fuel cost over the past few decades. By consideration of these undesirable effects, the automotive industry will move towards the electrified drivetrains for obtaining zero-emission and higher efficiency. In electrified vehicles, the drive is coupled entirely partially by e-motors powered by energy storage sources. The choice of the simple structured HESS topology is to make a better understanding of power flow and controlling circuits based on limits of existing energy storage sources and contribute to the electrification of the drive. The main intention of this paper, the excessive current which is required by the motor is to draw from the ultra-capacitor without burdening of battery. This will improve the efficiency of the storage sources and also smooth running of the drive. This document examines the feasibility and capacity of a HESS which integrates the battery and ultra-capacitor units and controlling through PI and Fuzzy controllers.
An Extensive Study on Machine Learning based Battery Health Estimation
Authors: Bhagavatula Sai Vasudeva , Bharadwaj Yellamraju Venkata Rupesh , Eltem Karthik Chandra , Babu Bobba Phaneendra , Marati Naveen Kumar
Published: October 2021

This manuscript is a comparative study on various machine learning Regression methods like Decision Tree and Random Forest and SVM and other improvised methods along with unsupervised methods like Reinforcement learning, ANN methods like DNN are also discussed along with advanced methods like GRU, CNN, LSTM for estimating the battery health in order to estimate its life which is used in the modern-day technology of Battery Management System. The evolution of the present day BMS bought a great opportunity to study more about adaptive learning systems as it provides greater efficiency and tunes itself basing on environmental changes for battery health estimation studying on various methods on the subsets of artificial intelligence can be helpful to build more accurate correlation between the input and output. Adaptive learning even having a self-adjusting feature the computational limitations and the data being used is also important in producing correct result with a promising accuracy, so multiple algorithms, architectures and models are studied for better understanding in order to come to conclusions for selecting the apt model for satisfying results. Compared to other conventional methods Artificial Intelligence and their subsets learn from the error and adopt which outperforms other models in accuracy.
Lead removal using Fallen Coffee Plant Leaves (FCPL) powder: Characterization, Equilibrium, Kinetics and Thermodynamic studies
Authors: Tukaram Bai M , Raju Ch. I. A. , Sridevi V , Chittibabu Nalluri , Venkateswarlu P
Published: October 2021

Biosorption of lead onto Fallen Coffee Plant leaves (FCPL) powder from an aqueous solution was studied in the present study. The characterization of FCPL powder was done by FTIR, XRD, SEM and BET. The equilibrium agitation time for lead biosorption is 60 min. The optimum pH and dosage values are 5.2 and 20 g/L respectively. In the range of variables studied, percentage biosorption is increased from 75.1 to 95.5 %. The maximum uptake capacity of 3.664 mg/g is obtained at 303 K. In the present investigation the equilibrium data was well explained by Langmuir, Redlich-Peterson with a correlation coefficient of 0.99, and followed by Temkin and Freundlich isotherms. The kinetic studies reveal that the biosorption system obeyed the pseudo second order kinetic model by considering the correlation coefficient value as 0.99. From the values of ∆S, ∆H and ∆G it is observed that the biosorption of lead onto Fallen coffee plant leaves(FCPL) powder was irreversible, endothermic and spontaneous.