bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Green Gasoline: Existing Standards
Authors: Marliza T. S. , Adira W. K. Wan Nor , Hakim L. Azizul , Razali S. Muhammad
Published: October 2023

This chapter summarizes the existing standard methods for the characterization of green gasoline. In order to commercialize green gasoline, this fuel must be able to comply with the standards for a combustion engine fuel compatible with ASTM D4814 specifications in the USA and EN 228 specifications in Europe. There are 30 ASTM test methods for conventional gasoline and seven of them are important test methods for green gasoline. These seven test methods are described in this chapter where the green gasoline needs to meet the required specifications before being used in gasoline engines.
Green Anti-knock Agents for Enhancement of Gasoline Performance
Authors: Tyagi Uplabdhi , Aslam Mohammad , Sarma Anil Kumar
Published: October 2023

Anti-knock agents are added to gasoline to increase its octane number and to reduce engine knocking. Engine knocking is a pinging noise that occurs when the fuel–air mixture in the combustion chamber detonates prematurely. This can cause damage to the engine parts and reduce its efficiency. Earlier, lead compounds were widely used as anti-knock agents in gasoline, but have been phased out owing to their negative effects on human health and the environment. Therefore, various alternative compounds have been introduced as anti-knock agents, including aromatic hydrocarbons, fuel oxygenates and organometallic compounds. Green gasoline itself can act as an anti-knock agent if used in appropriate proportions in petroleum gasoline blends. The use of these compounds is subject to regulations, which vary depending on the country and its specific legislation.
Modelling PCB-153 in northern ecosystems across time, space, and species using the nested exposure model
Authors: Krogseth Ingjerd S. , Breivik Knut , Frantzen Sylvia , Nilsen Bente M. , Eckhardt Sabine , Nøst Therese H. , Wania Frank
Published: October 2023

NEM enables an integrated, quantitative analysis of the whole continuum of processes linking global contaminant emissions with exposure of organisms.
Using a cotton thread-based colorimetric sensor modified by carboxymethylcellulose and cuprizone with smartphone detection for quantification of copper
Authors: de Oliveira Josiane Lopes , Toito Suarez Willian , dos Santos Vagner Bezerra , da Silva Leandro Paulo , Capitán-Vallvey Luis Fermin
Published: October 2023

Cotton thread with carboxymethylcellulose and cuprizone as a colorimetric sensor for quantification of copper in beverages and water using a digital image from a smartphone.
Fragmentation dynamics and absolute dissociative electron attachment cross sections in the low energy electron collision with ethanol
Authors: Paul Anirban , Ghosh Soumya , Nandi Dhananjay
Published: October 2023

The low-energy electron collision with ethanol produces different fragment anions. Production of C2H3O− in dissociative electron attachment occures sequentially by hydrogen migration.
How a few help all: cooperative crossing of lipid membranes by COSAN anions
Authors: Malaspina David C. , Teixidor Francesc , Viñas Clara , Faraudo Jordi
Published: October 2023

COSAN anions are able to efficiently cross bilayers impermeable to ions due to a cooperative flip-flop mediated by transient, elongated structures within the membrane, implying a novel ion permeation mechanism with self-assembly characteristics.
Controlled synthesis of photoresponsive bismuthinite (Bi2S3) nanostructures mediated through a new 1D bismuth-pyrimidylthiolate coordination polymer as a molecular precursor
Authors: Kulkarni Atharva Yeshwant , Karmakar Gourab , Shah Alpa Y. , Nigam Sandeep , Kumbhare Gayatri , Tyagi Adish , Butcher Raymond J. , Chauhan Rohit Singh , Kumar N. Naveen
Published: October 2023

A newly synthesized 1D bismuth-pyrimidylthiolate coordination polymer has been utilized as a single-source molecular precursor for the synthesis of Bi2S3 nanostructures with precise control over size, morphology and optical properties.
Rational design of efficient photosensitizers based on cyclometalated iridium(iii) complexes with 2-arylbenzimidazole and aromatic 1,3-diketone ligands
Authors: Tatarin Sergei V. , Meshcheriakova Elizaveta A. , Kozyukhin Sergey A. , Emets Victor V. , Bezzubov Stanislav I.
Published: October 2023

Design and synthesis of cyclometalated iridium(iii) complexes with 2-arylbenzimidazoles and 1,3-diketones demonstrating strong light absorption up to 500 nm, the desired excited state localization and good photovoltaic performance are reported.
Combined stripping-struvite precipitation process for treating high ammonia-nitrogen wastewater generated by the craft of vanadium extraction
Authors: Zhu Siqin , Ye Guohua , Kang Xuanxiong
Published: October 2023

Unlocking Sustainable Solutions! A significant achievement in high-concentration ammonia–nitrogen removal from vanadium extraction wastewater is reported through an innovative combination of stripping and struvite precipitation methods.
Structural analyses of carbon films deposited at different total mass rates in a hot-filament CVD system
Authors: Ali Mubarak
Published: October 2023

XRR and XRIR analyses of the deposited carbon films showing different peaks related to the diamond and graphite state atoms.
A numerical study on the flow of water-based ternary hybrid nanomaterials on a stretchable curved sheet
Authors: Shinwari W. , Hayat T. , Abbas Z. , Momani S.
Published: October 2023

Nonlinear non-dimensional systems are solved numerically by using shooting method with Runge–Kutta–Fehlberg technique.
Correction: Recent trends in carbon nanotube (CNT)-based biosensors for the fast and sensitive detection of human viruses: a critical review
Authors: Meskher Hicham , Mustansar Hussain Chaudhery , Thakur Amrit Kumar , Sathyamurthy Ravishankar , Lynch Iseult , Singh Punit , Han Tan Kim , Saidur Rahman
Published: October 2023

Correction for ‘Recent trends in carbon nanotube (CNT)-based biosensors for the fast and sensitive detection of human viruses: a critical review’ by Hicham Meskher et al., Nanoscale Adv., 2023, 5, 992–1010, DOI: https://doi.org/10.1039/D2NA00236A.
Non-metal doped Fe–C2N as an electrocatalyst for oxygen reduction reaction: a density functional calculation
Authors: Shu Dan , Wang Dan , Wang Yan
Published: October 2023

The introduction of non-metal atoms had a variety of synergistic effects on regulating the charge redistribution of Fe active sites and FeC–C2N showed the most prominent ORR performance.
Shape complementarity processes for ultrashort-burst sensitive M13–PEG–WS2-powered MCF-7 cancer cell sensors
Authors: Meivita Maria P. , Go Shao-Xiang , Mozar Fitya S. , Li Lunna , Tan Yaw Sing , Bajalovic Natasa , Loke Desmond K.
Published: October 2023

We control the electrical character of a 2D nanomaterial by using the integration of the phage-conjugated polymer and the nanosheet through shape-complementarity processes and design a sensor system for the rapid, sensitive detection of MCF-7 cells.
Femtosecond-laser-patterned origami Janus membrane toward enhanced water fog harvesting
Authors: Yao YanSheng , Peng Tao , Peng Yubin , Meng Qiangsong , Zhu Suwan , Hu Yanlei , Li Jiawen , Wu Dong
Published: October 2023

An origami patterned Janus membrane exhibits an exceptional water collection rate of 267%, which should facilitate the development of efficient fog collection systems.
Platinum–palladium-on-reduced graphene oxide as bifunctional electrocatalysts for highly active and stable hydrogen evolution and methanol oxidation reaction
Authors: Feng Yingliang , Zhu Lihua , Pei An , Zhang Sifan , Liu Kunming , Wu Fengshun , Li Wenqi
Published: October 2023

PtPd/rGO-2 shows exceptional excellent catalytic performance and good long-time stability for the HER, MOR, and MOR ∥ HER, which is attributable to the appropriate metal contents and the synergistic effect of Pt, Pd, and Pt–Pd alloy nanoparticles.
3D printed filtration and separation devices with integrated membranes and no post-printing assembly
Authors: Clark Molly J. , Garg Tushar , Rankin Kathryn E. , Bradshaw Darren , Nightingale Adrian M.
Published: October 2023

Three-dimensional (3D) printing, is used to produce functional filtration and separation devices incorporating commercial porous membranes. It is an accessible, quick, and user-friendly tool for making bespoke chemical processing devices.
New insights into controlling radical migration pathways in heme enzymes gained from the study of a dye-decolorising peroxidase
Authors: Lučić Marina , Wilson Michael T. , Pullin Jacob , Hough Michael A. , Svistunenko Dimitri A. , Worrall Jonathan A. R.
Published: October 2023

We show that radical migration in a heme peroxidase can be controlled and directed to a rationally designed site through simply removing an oxygen atom form the protein structure.
Carboxylate BODIPY integrated in MOF-5: easy preparation and solid-state luminescence
Authors: Tran Alexis , Leroux Marion , Michelin Clément , Réveret François , Boyer Damien , Cisnetti Federico
Published: October 2023

This study presents a facile room-temperature route to prepare new MOF@BODIPY hybrid materials. Their luminescence was associated with an efficient dispersion allowing high photoluminescence quantum yield in the solid state.
The role of peroxidasin in solid cancer progression
Authors: Wyllie Kaitlin , Panagopoulos Vasilios , Cox Thomas R.
Published: October 2023

Peroxidasin is a heme-containing peroxidase enzyme that plays a vital role in the cross-linking of collagen IV molecules in basement membranes. Collagen IV cross-links are essential for providing structure and mechanical stability throughout tissue development, homeostasis, and wound healing. During cancer progression, the basement membrane is degraded, and proteins typically found in the basement membrane, including peroxidasin and collagen IV, can be found spread throughout the tumour microenvironment where they interact with cancer cells and alter cell behaviour. Whilst peroxidasin is reported to be up-regulated in a number of different cancers, the role that it plays in disease progression and metastasis has only recently begun to be studied. This review highlights the current literature exploring the known roles of peroxidasin in normal tissues and cancer progression, regulators of peroxidasin expression, and the reported relationships between peroxidasin expression and patient outcome in cancer.
Effectiveness of Stuttering Modification Treatment in School-Age Children Who Stutter: A Randomized Clinical Trial
Authors: Kohmäscher Anke , Primaßin Annika , Heiler Sabrina , Avelar Patricia Da Costa , Franken Marie-Christine , Heim Stefan
Published: October 2023

Purpose:

This study investigated the effectiveness of the stuttering modification intervention Kinder Dürfen Stottern (KIDS) in school-age children who stutter.

Method:

Seventy-three children who stutter were included in this multicenter, two-group parallel, randomized, wait-list controlled trial with a follow-up of 12 months. Children aged 7–11 years were recruited from 34 centers for speech therapy and randomized to either the immediate-treatment group or the 3 months delayed-treatment group. KIDS was provided by 26 clinicians who followed a treatment manual. Although the primary outcome measure was the impact of stuttering (Overall Assessment of the Speaker's Experience of Stuttering–School-Age [OASES-S]), the secondary outcomes included objective and subjective data on stuttering severity.

Results:

At 3 months postrandomization, the mean score changes of the OASES-S differed significantly between the experimental (
n
= 33) and control group (
n
= 29;
p
= .026). Furthermore, treatment outcomes up to 12 months were analyzed (
n
= 59), indicating large effects of time on the OASES-S score (
p
< .001, partial η
2
=
.324). This was paralleled by significant improvements in parental ratings and objective ratings (stuttering severity, frequency, and physical concomitants).

Conclusions:

The significant short-term treatment effects in the OASES-S are in line with the (initial) focus of KIDS on cognitive and affective aspects of stuttering. Over 12 months, these changes were maintained and accompanied by behavioral improvements. The results suggest that individual treatment with KIDS is an adequate treatment option for this age group.

Supplemental Material:

https://doi.org/10.23641/asha.24207864
Overview of Issues in Autism Intervention Research: Research Design and Reporting
Authors: Bottema-Beutel Kristen , Sandbank Micheal , Woynaroski Tiffany
Published: October 2023

Purpose:

Neurodiversity-affirming practices for people with autism need to be backed by rigorous evidence of effectiveness. We discuss aspects of research design and reporting in autism intervention research that should be considered by intervention providers when providing evidence-based practices to support people with autism. We describe risks of bias, which are features of the study design that make it more likely that ineffective interventions will show evidence of effectiveness; social validation, which are procedures that determine community members' views on the intervention goals, procedures, and outcomes; the design of intervention outcome variables, including the extent to which they index lasting, meaningful change; adverse event monitoring and reporting, which is the extent to which researchers track and transparently communicate negative events that occur during or after participation in the intervention; and conflicts of interest (COIs), which are relationships held by the researcher that could result in positive researcher benefits for showing particular outcomes in their studies. We discuss several systematic reviews and evaluations of autism intervention literature that have explored the extent to which researchers have adhered to ethical guidelines around each of these issues.

Conclusions:

Risks of bias are prevalent in this literature, social validation is rarely rigorously conducted, and positive intervention outcomes tend to index narrow, specific change. Additionally, researchers rarely monitor or report adverse events despite evidence that they occur, and they often fail to disclose COIs or falsely claim not to have them. Clinicians and researchers should be aware of these features of autism intervention studies, so that they can make informed decisions as new, and hopefully improved, research is produced.
A novel fault location strategy based on Bi‐LSTM for MMC‐HVDC systems
Authors: Inwumoh Jude , Baguley Craig , Madawala Udaya , Gunawardane Kosala
Published: October 2023

The integration of modular multilevel converters (MMCs) with high voltage direct current (HVDC) transmission systems is an efficient method for transporting electricity from distant renewable energy sources to demand centres. However, MMC‐HVDC systems face reliability challenges during DC overcurrent faults, often caused by component failures that can lead to HVDC network shutdowns. Consequently, a reliable fault location approach is crucial for grid protection and restoration, aiding in fault isolation and alternate power flow identification. Conventional fault location methods struggle with manual protective threshold setting, susceptibility to fault resistance and noise, and the need for communication channels, resulting in signal delays. In multi‐terminal HVDC networks, fault location becomes even more complex due to poor selectivity and sensitivity in traditional schemes. This study proposes a robust fault location approach based on bidirectional long short‐term memory (bi‐LSTM). The method offers a simplified decision‐making model with low computational requirements, utilizing fault features from one end of the network, eliminating the need for a communication channel. Remarkably, this approach achieves high fault location accuracy, even with varying fault types, resistances, and noise levels, as demonstrated by an MSE of 0.006 and a percentage error below 1% in simulations conducted using a real‐time simulator with MATLAB/Simulink.
Expansion and ongoing cosmic ray acceleration in HESS J1731−347
Authors: Doroshenko V. , Pühlhofer G. , Santangelo A.
Published: October 2023

Diffusive shock acceleration in supernova remnants (SNRs) is considered one of the prime mechanisms of galactic cosmic ray (GCR) acceleration. It is still unclear, however, whether SNRs can contribute to the GCR spectrum up to the “knee” (1 PeV) band as acceleration to such energies requires an efficient magnetic field amplification process around the shocks. The presence of such a process is challenging to test observationally. Here, we report on the detection of fast variability in the X-ray synchrotron emission from the forward shock in the SNR HESS J1731−347, which implies the presence of a strong (∼0.2 mG) field exceeding background values, and thus of effective field amplification. We also report a direct measurement of the high forward shock expansion velocity of 4000–5500 km s−1, confirming that the SNR is expanding in a tenuous wind bubble blown by the SNR progenitor, is significantly younger (2.4–9 kyr) than previously assumed by some authors, and only recently started interacting with the dense material outside of the bubble. We finally conclude that there is strong evidence for ongoing hadronic GCR acceleration in this SNR.
Cold atomic gas identified by H I self-absorption
Authors: Syed J. , Beuther H. , Goldsmith P. F. , Henning Th. , Heyer M. , Klessen R. S. , Stil J. M. , Soler J. D. , Anderson L. D. , Urquhart J. S. , Rugel M. R. , Johnston K. G. , Brunthaler A.
Published: October 2023

Context. Stars form in the dense interiors of molecular clouds. The dynamics and physical properties of the atomic interstellar medium (ISM) set the conditions under which molecular clouds and eventually stars form. It is, therefore, critical to investigate the relationship between the atomic and molecular gas phase to understand the global star formation process.

Aims. Using the high angular resolution data from The H I/OH/Recombination (THOR) line survey of the Milky Way, we aim to constrain the kinematic and physical properties of the cold atomic hydrogen gas phase toward the inner Galactic plane.

Methods. H I self-absorption (HISA) has proven to be a viable method to detect cold atomic hydrogen clouds in the Galactic plane. With the help of a newly developed self-absorption extraction routine (astroSABER), we built upon previous case studies to identify H I self-absorption toward a sample of giant molecular filaments (GMFs).

Results. We find the cold atomic gas to be spatially correlated with the molecular gas on a global scale. The column densities of the cold atomic gas traced by HISA are usually on the order of 1020 cm−2 whereas those of molecular hydrogen traced by 13CO are at least an order of magnitude higher. The HISA column densities are attributed to a cold gas component that accounts for a fraction of ~5% of the total atomic gas budget within the clouds. The HISA column density distributions show pronounced log-normal shapes that are broader than those traced by H I emission. The cold atomic gas is found to be moderately supersonic with Mach numbers of approximately a few. In contrast, highly supersonic dynamics drive the molecular gas within most filaments.

Conclusions. While H I self-absorption is likely to trace just a small fraction of the total cold neutral medium within a cloud, probing the cold atomic ISM by the means of self-absorption significantly improves our understanding of the dynamical and physical interaction between the atomic and molecular gas phase during cloud formation.
The role of radial migration in open cluster and field star populations withGaiaDR3
Authors: Viscasillas Vázquez C. , Magrini L. , Spina L. , Tautvaišienė G. , Van der Swaelmen M. , Randich S. , Sacco G. G.
Published: October 2023

Context.The survival time of a star cluster depends on its total mass, density, and thus size, as well as on the environment in which it was born and in which lies. Its dynamical evolution is influenced by various factors such as gravitational effects of the Galactic bar, spiral structures, and molecular clouds. Overall, the factors that determine the longevity of a cluster are complex and not fully understood.

Aims.This study aims to investigate whether open clusters and field stars respond differently to the perturbations that cause radial migration. In particular, we aim to understand the nature of the oldest surviving clusters.

Methods.We compared the time evolution of the kinematic properties of twoGaiaDR3 samples. The first sample is composed of ∼40 open clusters and the second one of ∼66 000 main sequence turn off field stars. Both of the samples are composed of stars selected with the same quality criterion, and they belong to the thin disc, are in a similar metallicity range, are located in the same Galactocentric region [7.5–9 kpc], and have ages greater than 1 Gyr. We performed a statistical analysis comparing the properties of the samples of the field stars and of the open clusters.

Results.A qualitative comparison of kinematic and orbital properties revealed that clusters younger than 2–3 Gyr are more resistant to perturbations than field stars, and they move along quasi-circular orbits. Conversely, clusters older than approximately 3 Gyr have more eccentric and inclined orbits than isolated stars in the same age range. Such orbits lead the older clusters to reach higher elevations on the Galactic plane, maximising their probability to survive several more gigayears. A formal statistical analysis revealed that there are differences among the time evolution of most of the kinematic and orbital properties of the field stars and open clusters. However, the comparison between some properties (e.g.,VϕandLZ) do not reach a sufficient statistical significance.

Conclusions.Our results suggest that the oldest surviving clusters are usually more massive and move on orbits with a higher eccentricity. Although they are still reliable tracers of the Galaxy’s past composition, they do not reflect the composition of the place where they are currently found. Therefore, we cannot avoid considering kinematic properties when comparing data and models of chemical evolution and also taking into account the intrinsic differences between clusters and isolated stars. To validate the results, new studies that increase the sample of open clusters, especially at older ages, are needed.
White dwarf Random Forest classification through Gaia spectral coefficients
Authors: García-Zamora Enrique Miguel , Torres Santiago , Rebassa-Mansergas Alberto
Published: October 2023

Context. The third data release of Gaia has provided approximately 220 million low resolution spectra. Among these, about 100 000 correspond to white dwarfs. The magnitude of this quantity of data precludes the possibility of performing spectral analysis and type determination by human inspection. In order to tackle this issue, we explore the possibility of utilising a machine learning approach, based on a Random Forest algorithm.

Aims. Our goal is to analyse the viability of the Random Forest algorithm for the spectral classification of the white dwarf population within 100 pc from the Sun, based on the Hermite coefficients of Gaia spectra.

Methods. We utilised the assigned spectral type from the Montreal White Dwarf Database for training and testing our Random Forest algorithm. Once validated, our algorithm model was applied to the rest of the unclassified white dwarfs within 100 pc. First, we started by classifying the two major spectral type groups of white dwarfs: hydrogen-rich (DA) and hydrogen-deficient (non-DA). Next, we explored the possibility of classifying the various spectral subtypes, including the secondary spectral types in some cases.

Results. Our Random Forest classification presented a very high recall (>80%) for DA and DB white dwarfs, and a very high precision (>90%) for DB, DQ, and DZ white dwarfs. As a result we have assigned a spectral type to 9446 previously unclassified white dwarfs: 4739 DAs, 76 DBs (60 of them DBAs), 4437 DCs, 132 DZs, and 62 DQs (nine of them DQpec).

Conclusions. Despite the low resolution of Gaia spectra, the Random Forest algorithm applied to the Gaia spectral coefficients proves to be a highly valuable tool for spectral classification.
The existence of hot X-ray onsets in solar flares
Authors: Battaglia Andrea Francesco , Hudson Hugh , Warmuth Alexander , Collier Hannah , Jeffrey Natasha L. S. , Caspi Amir , Dickson Ewan C. M. , Saqri Jonas , Purkhart Stefan , Veronig Astrid M. , Harra Louise , Krucker Säm
Published: October 2023

Context. It is well known among the scientific community that solar flare activity often begins well before the main impulsive energy release. However, a consistent explanation for this phenomenon has not yet been established.

Aims. Our aim is to investigate the earliest phase of four distinct flares observed by Solar Orbiter/STIX and determine the relationships of the newly heated plasma to flare structure and dynamics.

Methods. The analysis focuses on four events that were observed from both Earth and Solar Orbiter, which allows for a comparison of STIX observations with those of GOES/XRS and SDO/AIA. The early phases of the events were studied using STIX and GOES spectroscopic analysis to investigate the evolution of the physical parameters of the plasma, including the isothermal temperature and emission measure. Furthermore, to determine the location of the heated plasma, STIX observations were combined with AIA images.

Results. The events with clear emission prior to the impulsive phase show elevated temperatures (> 10 MK) from the very beginning, which indicates that energy release started before any detection by STIX. Although the temperature shows little variation during the initial phase, the emission measure increases by about two orders of magnitude, implying a series of incrementally greater energy releases. The spectral analysis of STIX and GOES from the very first time bins suggests that the emission has a multi-thermal nature, with a hot component of more than 10 MK. Alternative heating mechanisms may be more significant during this phase, since nonthermal emission, as observed by STIX, is only detected later. STIX and AIA images reveal the presence of more compact sources of hot plasma early in the flare that originate from different locations with respect to the standard loop-top source that is observed later in the flare. However, because extended bremsstrahlung sources are much more difficult to detect than compact sources, there might be additionally heated plasma in the loop-top during hot onsets.

Conclusions. This analysis confirms the existence of “hot onsets”, with STIX detecting the hot onset pattern even earlier than GOES. These elevated temperatures imply that energy release actually begins well before any detection by STIX. Therefore, hot onsets may be significant in the initiation, early development, or even prediction of solar flares.
Smart Resume Analyser: A Case Study using RNN-based Keyword Extraction
Authors: Sruthi Patlolla , Adithya P.N.V.K.G. , Suleman M.D. , Kunal Pallerla , Gairola Surya Prakash
Published: October 2023

The pursuit of a reputable position in a company is a common goal shared by many people. For making this desire into reality their resume is the key to it. Making these resumes is a challenging task for many and modifying it is another time-consuming and burdensome task. Taking these problems into consideration and to make eye-catching resumes we have developed a smart resume analyser that can analyse the user’s resume, it intelligently identifies their skills and qualifications, enabling us to suggest the best-suited job titles for their profile. Furthermore, based on this analysis, our system generates recommendations for optimizing and enhancing the user’s resume, making it more appealing to potential employers. Through the utilization of NLP and ML technologies, our resume analyser provides personalised and effective solutions to job seekers, helping them stand out in today’s competitive job market. The information on the resume can be analysed and understood easily as NLP has the capability to understand and parse the information on the resume and extract the desired information efficiently. With the additional help from Python and its packages like streamlit, pymysql, etc. we can store the extracted data and give a rating based on the analysis of the stored data. After the rating is generated, we recommend some modifications like: adding sentences like my hobbies are, my goal is, add objectives, and add declaration and more. We also suggest a few sources through which the user can enhance their skills.
Automated Brain Tumour Classification using Deep Learning Technique
Authors: Kiran Kumar M. , Sree Naga Sreeja D. , Sadiq Samiya , Manisha D. , Jain Abhishek , Madhu Bhukya
Published: October 2023

Brain Tumour is a severe condition caused due to abnormal growth of cells in the brain. Brain Tumour is broadly classified into two categories namely Malignant (Cancerous) and Benign (Non-Cancerous). As tumour grows, the pressure within the skull can increase which can damage the brain and be life-threatening. Early detection and classification of the brain tumours is important as it helps to select the most appropriate treatment for saving the patient’s life. Usually, Brain Tumour Detection can be done manually by the doctors or use machine learning models in case of MRI images of the brain. In literature, it is identified that deep learning techniques such as CNN, DCNN and RNN show good results in image processing applications. This paper aims to detect and classify the Brain Tumours effectively using CNN deep learning techniques. The dataset is collected from Kaggle. The proposed method achieved an accuracy of 93.5% and 98.4% with CNN and Resnet50 respectively.
Predicting Smartphone Vision Syndrome: A Feasible Approach using Machine Learning Algorithms
Authors: Annapurna T. , Rajeswari P.V.G.S. , Likitha Aeloorie , Deekshitha Gadi , Sharma Sonal , Venkat Rao Y. , Ram Kumar R.P.
Published: October 2023

Smartphone Vision Syndrome (SVS) is an evitable problem for people who spend a great deal of time watching digital screens. It is a major concern for rapid growth in technology where the burden is significantly greater due to factors such as limited access to and use of personal protective equipment, as well as lesser break time. The objective of the model is to achieve a feasible and higher level of eye health for people who are working long hours with digital screens. The dataset is obtained through an online survey form containing metrics that contribute to the occurrence of SVS. After applying Machine Learning algorithms, namely Logistic Regression, Random Forest Classifier, Naïve Bayes and Support Vector Machine (SVM), the model’s overall performance is assessed using the test sample. Accuracies obtained by Random Forest, Support Vector Machine, Logistic Regression, Naïve Bayes, and Gaussian Naïve Bayes are 98.75%, 97.5%, 77.5%, 95% and 96.25%.
Automated Cyberbullying Activity Detection using Machine Learning Algorithm
Authors: Yoganand Bharadwaj Vedadri , Likhitha Vasamsetti , Vardhini Vootnoori , Uma Sree Asritha Adari , Dhyani Saurabh , Lakshmi Kanth M.
Published: October 2023

Cyberbullying is the use of technology to harass, intimidate, or harm another person by making hurtful comments, sending threatening messages to humiliate someone in social media. It is important to recognize the signs of cyberbullying activities and takes steps to prevent it. Automated Machine Learning algorithms and Text mining concepts for detecting and classifying bullying messages in social media environment. The abusive texts are clustered using Multinomial Naïve Bayes, LinearSVC, Logistic Regression, K-Nearest neighbour to build a classifier from training datasets. Implementation uses Suspicious-communications-on-social-platforms dataset.
Feasible Sentiment Analysis of Real Time Twitter Data
Authors: Karuna G. , Anvesh Pavuluri , Sharath Singh Chiranji , Ruthvik Reddy Kommula , Kumar Shah Praveen , Siva Shankar S.
Published: October 2023

Sentiment analysis plays a significant role in understanding public opinion, trends, and sentiments expressed on social media platforms. In this paper, we focus on performing sentiment analysis on real-time Twitter data to gain insights into the sentiments related to specific topics or events, we collect a stream of tweets based on predefined keywords or hashtags. The collected tweets undergo pre-processing steps to clean and standardize the text for sentiment analysis. We employ machine learning classify the sentiments expressed in tweets, utilizing sentiment lexicons and training data as references. Real-time sentiment analysis is performed as new tweets are collected, enabling continuous monitoring and analysis of public sentiment. The sentiment analysis results are visualized through informative visualizations such as sentiment distribution charts and sentiment trends over time. Additionally, we focus on topic-specific analysis by filtering tweets based on relevant keywords or hashtags, providing deeper insights into sentiments related to specific subjects. The paper faces challenges such as noisy and informal text, ambiguity in sentiment expression, and handling large volumes of real-time data. Addressing these challenges, we aim to develop an effective sentiment analysis system that provides valuable insights into public sentiment and supports decision-making processes in various domains.
Automated System for Bird Species Identification Using CNN
Authors: Siri Dharmapuri , Desu Sashank , Alladi Karthik , Swathi Duggi , Singh Swati , Srilakshmi Vellanki
Published: October 2023

There are around 11000 different bird species in the globe. Rarely are certain bird species encountered. Bird identification is a challenging task that usually leads to unclear labelling. When presented a picture of a bird, even professional bird watchers differ on the species. Despite having the same basic components across all bird species, form and appearance can vary greatly. Intraclass variance is substantial due to variations in lighting and backdrop, as well as a wide range of instances. Additionally, visual recognition of birds by humans is more comprehensible than audible recognition of birds. Consequently, the convolutional neural networks(CNN) is utilized for an automated bird species identification system. CNNs are a powerful Deep Learning ensemble that have shown to be effective in image processing. The dataset is used for both training and testing of a CNN system that classifies bird species. This will lead to quick identification of the bird species using an automated process.
Deep Learning-based Speech Emotion Recognition: An Investigation into a sustainably Emotion-Speech Relationship
Authors: Pavithra Avvari , Ledalla Sukanya , Devi J. Sirisha , Dinesh Golla , Singh Monika , Reddy G. Vijendar
Published: October 2023

Speech Emotion Recognition (SER) poses a significant challenge with promising applications in psychology, speech therapy, and customer service. This research paper proposes the development of an SER system utilizing machine learning techniques, particularly deep learning and recurrent neural networks. The model will be trained on a carefully labeled dataset of diverse speech samples representing various emotions. By analyzing crucial audio features such as pitch, rhythm, and prosody, the system aims to achieve accurate emotion recognition for novel speech samples. The primary objective of this paper is to contribute to the advancement of SER by improving accuracy, reliability, and gaining deeper insights into establishing a sustainable complex relationship between emotions and speech. This innovative system has the potential to facilitate the practical implementation of emotion recognition technologies across multiple domains.
Secure and Sustainable Decentralized Cloud Using IPFS
Authors: Abhilash P.K. , K. Chidananda , Sandeep K. , Reddy Molaka Nikhil , Awasthi Yogesh Kumar , Rajabhishek Samanthakurthi
Published: October 2023

Our paper focuses on the implementation of a decentralized cloud storage system using the IPFS (InterPlanetary File System) technology. The project aims to provide an alternative to centralized cloud storage by decentralization, enhanced security, and user control over data which is a more sustainable solution than the existing system. In a centralized cloud storage system, data is stored on a single server controlled by a service provider. This centralized approach poses potential risks such as single points of failure, data breaches, and reliance on the trustworthiness of the provider. In contrast, our decentralized cloud storage system distributes data across a network of interconnected nodes. Each node stores a portion of the data, ensuring redundancy and availability. By leveraging IPFS, which utilizes a distributed hash table and content- addressed storage, our system ensures data integrity, fault tolerance, and censorship resistance. Users of our system have greater control over their data. They can participate in the network by contributing their storage resources and accessing their files securely. The project implementation includes features such as file upload, download, retrieval based on IPFS hashes, and user authentication. It also provides functionalities like displaying connected peers, managing file metadata, and enabling file deletion. Overall, our project showcases the potential of decentralized cloud storage, exemplifying its benefits and illustrating how it can be implemented using IPFS technology.
Motorized Smart Roofing System using IoT for Sustainable development
Authors: Swaraj P. Prathibha , Shivuni Deekshitha , Chatragadda Lasya , Ettom Srinidhi , Srivastava V.K. , Bindu K. Hima
Published: October 2023

Climate change caused by human activity has led to abnormal rainfall in India. Agriculture is a crucial source of income for the Indian Government and the abnormal rainfall patterns pose a significant threat to the industry. These fluctuations in rainfall have caused damage to homes, and infrastructure, wash away crops and destroy water sanitation. Unusual rainfalls are also affecting the Rooftop restaurants. The Government, Roof Top restaurant owners, and farmers are all seeking solutions to address the impact of climate change. One potential solution is the development of a Smart Roofing System that can detect rainfall and respond accordingly. The Proposed system would use a Rainfall Sensor to detect rain, which would then send signals to a Wi-Fi-enabled board. The board would then activate dc motors, opening the roof to protect against rainfall. When the Rainfall Sensor detects no rain, it sends a signal to the board to reverse the direction of the motors, closing the roof. The implementation of this Smart Roofing System could help minimize the damage caused by unusual rainfall patterns and help towards the development of a sustainable environment. With a Smart Roofing System in place, roof-top restaurant owners could continue their service during periods of heavy rain, ensuring minimal disruption to their business. Overall, the development of a Smart Roofing System could help protect India’s infrastructure, agriculture, and economy from the adverse effects of climate change with minimal cost.
Advanced Composite Manufacturing using Additive Manufacturing and Robotic Techniques
Authors: Gupta Nakul , Antham Chandra Prakash , Das Karabi Kalita , Goel Radha , Zabibah Rahman S. , Kumar Manish
Published: October 2023

In the realm of mechanical engineering, the adoption and integration of cutting-edge technologies promise unprecedented advancements in material science and production processes. This paper delves into the pioneering realm of Advanced Composite Manufacturing leveraging both Additive Manufacturing (AM) and Robotic Techniques. Exploiting the intrinsic merits of AM, such as enhanced design freedom, reduced lead times, and intricate detailing, the research synergizes these advantages with the precision, speed, and repeatability offered by robotic mechanisms. The culmination of these methods allows for the fabrication of composite structures with unparalleled geometrical intricacy and tailored mechanical properties. Key insights from our exploration involve the optimization of AM parameters for composite materials, robotic path planning for efficient layering, and a holistic technique for integrated process control. Experimental evaluations signify marked improvements in terms of strength-to-weight ratios, production efficiency, and repeatability. Our findings pave the way for a new frontier in composite production, holding significant implications for industries ranging from aerospace to biomedical engineering. This study serves as a foundational step towards a paradigm shift in how we perceive and employ composite manufacturing in a progressively digital age.
Fabrication and Characterization of Nanoscale Metal-Organic Frameworks (MOFs) for Gas Storage and Separation
Authors: B. Santosh Kumar , Aswini Kilaru , Rao A. Kakoli , Alam Khursheed , Mashkour Muthana Saleh , Jain Alok
Published: October 2023

In recent years, Metal-Organic Frameworks (MOFs) have emerged as a promising class of materials for gas storage and separation applications due to their high surface area, tunable pore size, and chemical functionality. In this study, we report the successful fabrication and characterization of nanoscale MOFs for enhanced gas storage and separation performance. We synthesized a series of MOFs with varying metal nodes and organic linkers, and systematically investigated their structural, thermal, and chemical stability. Advanced characterization techniques, including X-ray diffraction, scanning electron microscopy, and gas adsorption isotherms, were employed to elucidate the structural and morphological features of the synthesized MOFs. The gas storage capacities of the MOFs were evaluated for hydrogen, methane, and carbon dioxide, revealing a significant enhancement in storage capacity compared to bulk MOFs. Furthermore, we investigated the gas separation performance of the MOFs for CO2/CH4 and CO2/N2 mixtures, demonstrating high selectivity and separation efficiency. The results of this study provide valuable insights into the design and fabrication of nanoscale MOFs for gas storage and separation applications, and pave the way for the development of next-generation materials for clean energy and environmental applications.
Investigating the Effects of Process Parameters on the Size and Properties of Nano Materials
Authors: Ravi Kumar M. , Reddy V. Phaninder , Meheta Ankit , Dhiyani Vivek , Al-Saady Fouad A. , Jain Alok
Published: October 2023

In recent years, the development of nano materials has garnered significant attention due to their unique properties and potential applications in various fields. However, the influence of process parameters on the size and properties of these materials remains a complex and largely unexplored area of research. In this study, we systematically investigate the effects of process parameters such as temperature, pressure, and reaction time on the size and properties of nano materials synthesized via a chemical vapor deposition (CVD) method. Using advanced characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD), we analyze the morphology, size distribution, and crystal structure of the synthesized nano materials. Our results reveal a strong correlation between the process parameters and the size of the nano materials, with temperature and pressure being the most influential factors. Furthermore, we observe a significant impact of the process parameters on the mechanical, thermal, and electrical properties of the nano materials. These findings provide valuable insights into the optimization of process parameters for the synthesis of nano materials with tailored properties, paving the way for their application in diverse fields such as electronics, energy storage, and catalysis. Our study contributes to the fundamental understanding of the relationship between process parameters and the properties of nano materials, offering a comprehensive framework for the design and synthesis of nano materials with desired characteristics.
Innovative Ceramic Forming Techniques for High-Strength, Low-Density Components
Authors: B. Santosh Kumar , Rashmi K. , Arora Deepika , Meenakshi  , Saeed Hazim Y. , Nijhawan Ginni
Published: October 2023

The development of high-strength, low-density ceramic components is a critical area of research in the field of material sciences and mechanical engineering, with potential applications in aerospace, automotive, and biomedical industries. In this paper, we present innovative ceramic forming techniques that enable the fabrication of high-performance ceramic components with unprecedented mechanical properties. We introduce a novel hybrid approach that combines the advantages of both additive manufacturing and traditional ceramic forming methods, such as slip casting and injection molding. By utilizing a customized ceramic slurry formulation and a modified 3D printing process, we successfully produced complex-shaped components with a uniform microstructure and enhanced mechanical properties. The resulting ceramic components exhibited a significant increase in flexural strength and fracture toughness compared to conventionally processed ceramics, while maintaining a low density. Furthermore, we conducted a comprehensive microstructural analysis using scanning electron microscopy (SEM) and X-ray diffraction (XRD) to elucidate the underlying mechanisms responsible for the improved mechanical performance. The findings of this study provide valuable insights into the potential of innovative ceramic forming techniques for the development of high-strength, low-density ceramic components, and pave the way for their widespread adoption in various engineering applications.
Fabrication of copper sulfide nanoparticles from boswellia ovalifoliolata leaf extract and their potential application of environmental pollutants removal, antibacterial activity and antioxidant activity
Authors: Narasaiah Boya Palajonna , Shareef S. Noormohammad , Rajesh Dasari , Chilakamarry Chitanya Reddy , Selvakumar P. , Koli Sakshi
Published: October 2023

A nanomaterial has played a major role in protecting the environment-related issues. The prime reason for that nanomaterials synthetics approach is greener pathway, without using any hazardous chemicals and solvents. A huge number of plant-mediated metal sulfide nanoparticle (especially, Copper sulfide) synthesis has been reported and is still successfully continuing, because of its cost effective manner, eco-friendly nature, simple approach, reaction was carried out room temperatur. The current reports to synthesis of Copper sulfide nanostuctured materials through the green patch way, using Boswellia Ovalifoliolata leaves extract. From the UV-Visible spectroscopy noticed nanoparticles absorbance value is around 325 nm. As identified by FT-IR spectroscopy, a variety sources of phytochemicals in the extract which are responsible for the reduction of metal ions and stabilizing of the nanoparticles. X-ray diffraction studies revealed that nanomaterials were crystalline in nature, average crystalline size around 11 nm. SEM revealed that nanoparticles are spherical in nature and average size is 38.43 nm. The current report emphasizes that the materials are an excellent catalyst activity for the reduction of environmental pollutant azo dyes, antibacterial and antioxidant activity. The current article highlights the reduction of the azo dyes, antibacterial and antioxidant activity so the nanomaterials are apromising for the reduction of polluntat dyes.