bluuebunny/crossref_metadata_2025 huggingface dataset:
The VLACHS of Eastern Serbia: Language and Society
Authors: Sorescu-Marinković Annemarie , Huțanu Monica
Published: April 2023

Published as a volume within the VLACH (Vanishing Languages and Cultural Heritage) series, this book represents the first in-depth sociolinguistic analysis of the Vlach community living in Eastern Serbia and of the language spoken by this community, Vlach Romanian. By integrating advances in the fields of language ideology, the online use of minority languages, linguistic landscape research, and language policy and planning, the book offers the reader a multi-faceted image of the state, status, and use of the language spoken by the Vlachs of Eastern Serbia, as well as of the ideologies accompanying it. Based on extensive fieldwork, detailed coverage of the texts written about and by the community, and relevant analysis of social media sites, this book generates insights on how the language shapes identity, influences ideology, sparks conflict, and impacts the linguistic landscape. This book is being released at a turning point in the history of the community: the revitalization measures and ongoing standardization process is dividing the ideologically divided Vlach society even more. In the coming years, it remains to be seen whether these changes will be able to challenge the stereotypes regarding the language and its speakers that still persist and whether these changes will bring about the greater use and visibility of Vlach Romanian. Due to the multiple aspects covered, the book will be of interest to sociolinguists and ethnologists working on Romance varieties and minorities in Serbia and more generally in the Balkans, but also to the members of the Vlach community.
Preventing extreme events in the urban environment using blue–Green infrastructure
Authors: Raczková Andrea , Wittmanová Réka , Hrudka Jaroslav , Škultétyová Ivona
Published: April 2023

The presented paper draws attention to the need to adapt urban areas to the negative impacts of climate change, more precisely to the solution of extreme rainfalls and long dry periods. The topic of rainwater management is popular worldwide due to the worsening situation in the field of climate change. Extreme weather fluctuations and torrential rains stress the urban hydrological cycle. Adaptation measures are proposed that mitigate the impact of climate change in cities. These also include measures to retain water in the country. The paper focuses on drainage density analysis, runoff coefficients and impermeability analysis, and the selection of appropriate measures to reduce surface runoff.
Contramedidas y uso de la fuerza armada: la ausencia de soluciones definitivas ¿o no?
Authors: Díaz-Galán Elena-C.
Published: April 2023

La comunidad internacional acepta la legalidad de las contramedidas en determinadas circunstancias y cuando se cumplan una serie de requisitos establecidos. En una sociedad descentralizada, como la internacional, tienen cabida las medidas de autotutela como las contramedidas, como mecanismo de aplicación del Derecho internacional. Una de las cuestiones más relevantes es aclarar si también se aceptan las contramedidas armadas, es decir, si serían conformes con el Derecho internacional las respuestas mediante el uso de la fuerza de un Estado frente a hechos anteriores de otro Estado de carácter ilícito. Esclarecer esta cuestión supone establecer la relación que corresponda entre las contramedidas y la norma que prohíbe el uso de la fuerza armada en las relaciones internacionales. La afirmación de que son ilícitas las represalias armadas debe ser contrastada a la luz de la práctica internacional. La labor realizada por la Comisión de Derecho internacional (cdi), los pronunciamientos de la jurisprudencia internacional y las posturas de la doctrina científica dominante apuntan hacia el reconocimiento de la ilicitud de las contramedidas armadas. Sin embargo, queda espacio para comprobar si esta solución está en armonía con lo que se deriva de la práctica internacional. Diversos asuntos de esta práctica ponen de relieve que serían lícitos comportamientos armados en contramedida, siempre que se cumplan ciertas exigencias y, sobre todo, cuando el hecho internacionalmente ilícito que provoca la contramedida encarna un empleo menor de la fuerza armada.
Основные направления документно-информационного обеспечения Украинской Галицкой армии в годы украинско-польской войны
Authors:   Narodowy Uniwersytet „Politechnika Lwowska”, Вовк Наталья
Published: April 2023

The author of the article considers the main way sand methods of document-information provision of Ukrainian Galician Army. Analyzed the basicorders and instructions of higher authorities of the Western Ukrainian People’s Republic,presented a brief description of the main pressing edition „Ukrajnskij golos”, „Republika”, „Ukrainskij prapor”, „Zolochivske slovo”, „Pokutskyj vistnyk”, „Vistnyk DSVS”. The article presents the main directions of development of telephone, telegraph and radio.
Tolerance as a moral imperative of multicultural society (on the example of Azerbaijan)
Authors:   Bakijski Uniwersytet Słowiański (Azerbejdżan), Akhundova Sevda
Published: April 2023

Obecny świat charakteryzuje globalny kryzys, upadek morale i środowiska naturalnego. Kwestie tolerancji są bardzo sporne. Tolerancja polega na chęci komunikacji i umiejętności nawiązywania kontaktów z osobami, które pod niektórymi względami różnią się od większości. Zapewnienie tolerancyjnych relacji jest uważane w prawie jako ochrona wartości społecznych, wolności i bezpieczeństwa jednostki. Humanistyczne ideały tej koncepcji często są powiązane z wykształceniem. Tolerancja według socjologów jest normą cywilizowanego kompromisu między konkurencyjnymi kulturami. Według definicji podanej w deklaracji zasad dotyczących tolerancji, „tolerancja” oznacza „szacunek, akceptację i prawidłowe zrozumienie dla wielkiej różnorodności kultur na świecie, naszych form wyrazu i sposobów bycia człowiekiem”. Definicja ta zakłada tolerancję dla innych ludzi bez względu na narodowość, rasę, kolor skóry, język, religię, miejsce zamieszkania. Tolerancja w stosunku do ludzi, którzy różnią się od nas przekonaniami i nawykami, wymaga zrozumienia, iż prawda nie może być prosta, gdyż ma wiele twarzy. Warto wskazać, że „w Azerbejdżanie tolerancja nie jest definicją naukową, Azerowie żyją tolerancją”. Dowodem tego jest fakt, że w Azerbejdżanie tolerancja etniczna i religijna znajduje swoje odzwierciedlenie w funkcjonowaniu wszystkich wyznań religijnych: islamu, chrześcijaństwa, judaizmu itp. W Baku działa ponad 20 różnych wspólnot kulturowych: polska, tacka, tatarska, niemiecka, bułgarska, ukraińska, grecka i inne.
Zespół Szkolny Morskich Sił Samoobrony Japonii w Polsce, Gdynia, 6–10 sierpnia 2013 roku
Authors:   Uniwersytet im. Adama Mickiewicza w Poznaniu, Franz Maciej
Published: April 2023

The School Team of the Japan Maritime Self-Defense Force in Poland, Gdynia, August 6th–10th, 2013
DES AND RANS MODELING OF PRIMARY ATOMIZATION IN A COAXIAL SWIRLING LIQUID-GAS JET
Authors: Choudhary Aniruddha , Narasimhamurthy Vagesh D.
Published: April 2023

This study assesses different turbulence modeling approaches for simulation of two-phase coaxial annular swirling jet flows. The problem selected from literature for comparison involves an analytical inlet profile for an annular liquid sheet sandwiched between two coaxial annular gaseous jets. The liquid-gas interface is resolved using the volume-of-fluid model with continuum surface force approximation. Reynolds-averaged Navier-Stokes simulations and detached eddy simulations (DES) are conducted to obtain transient multiphase numerical solutions. Different turbulence models explored include the k-ε renormalization group (RNG) with swirl modification, the Reynolds stress model (RSM), RSM with scale-adaptive simulation (RSM-SAS), and DES. Comparisons with the direct numerical results from literature suggest that the k-ε RNG and RSM approaches simulate only the streamwise shear of the liquid jet and are inadequate in capturing the swirling aspect of the jet flow and expected instabilities. DES can predict several expected features such as radial asymmetry, surrounding gas vortices causing jet instabilities, and eventual jet breakup with reasonable accuracy. While RSM-SAS predicts radial asymmetry, some jet instability, and is much more accurate than k-ε RNG and RSM, it fails to predict instabilities as good as DES and does not predict a complete jet breakup. RSM-based methods are found to be computationally very expensive compared to the k-ε RNG model, suggesting DES as the better alternative than RSM methods for such applications if resources are available.
CCT3 as a Diagnostic and Prognostic Biomarker in Cervical Cancer
Authors: Li Man , Zeng Jianmin , Chang Yuhuan , Lv Lili , Ye Guoliu
Published: April 2023

The chaperonin-containing TCP1 complex subunit 3 (CCT3) has been reported to be involved in the development and prognosis of many tumors, including cervical cancer (CC). This study aimed to analyze the expression and prognostic value of CCT3 in CC by bioinformatics and retrospective study. CCT3 gene expression profiles and clinical information in CC were downloaded from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. CCT3 expression was verified by quantitative real-time polymerase chain reaction (RT-qPCR), Western blot, and immunohistochemistry (IHC). Logistic regression and chi-square testing were used to analyze the relationship between CCT3 expression and the clinical characteristics of CC. Kaplan-Meier and Cox analyses were used to evaluate whether CCT3 affects the prognosis of CC. Nomogram and calibration curves were used to test the predictive value of CCT3. The expression of CCT3 in CC tissues was significantly upregulated compared with that in adjacent benign tissues, and was related to HPV16/18 infection, grade, and positive lymph nodes. High expression of CCT3 is associated with poor prognosis of CC and can be used as an independent risk factor for CC. The prognostic model based on CCT3 and CC clinical features has good predictive ability. CCT3 is overexpressed in CC, which is related to poor prognosis and expected to become a biomarker for CC.
Rheology of cement–sodium silicate grout containing bentonite and cellulose ether at high temperatures
Authors: Liu Rentai , Li Xiuhao , Li Shucai , Zhang Chunyu , Chen Mengjun , Du Canxun , Du Sanlin
Published: April 2023

Cement–sodium silicate grout (CSG) is widely used to control water inrush disasters, and its apparent viscosity considerably impacts its water-plugging effect. However, traditional grouting materials and methods are inappropriate for high-temperature environments as high temperatures can affect the grout's viscosity. To thicken grout and increase its apparent viscosity, viscosity-modifying admixtures can be used. In this work, two types of bentonite (calcium bentonite (Ca-B) and sodium bentonite (Na-B)) and hydroxyethyl methyl cellulose (HEMC) were used to modify CSG and laboratory tests were performed to evaluate the fluidity, gelation time and rheology. The results showed that both bentonites and the HEMC decreased fluidity and prolonged the gelation time. HEMC, Ca-B and Na-B decreased the fluidity by 46.8–60.4%, 12.5–31.5% and 17.7–39.1%, respectively, at different temperatures. HEMC, Ca-B and Na-B increased the gelation time by 23.8–50.1%, 23.3–71.4% and 20%–57.1%, respectively. Bentonite can partially resist high temperatures and improve the apparent viscosity of grout owing to its water-absorption capacity. Conversely, HEMC has a negative effect on apparent viscosity, which is attributed to the formation of a complex microstructure resulting from intermolecular cross-linking between the cement particles and HEMC, preventing the connection of sodium silicate.
Procedimiento para la acción de tutela actualizada
Authors:   Universidad Cooperativa de Colombia, Saavedra Ramírez Laura Cristina
Published: April 2023

Este ejercicio académico busca contribuir en la formación de los estudiantes frente a los diversos escenarios en que opera la presentación de acciones de tutela como mecanismo de protección constitucional. Desde su creación hace 30 años la acción de tutela se ha convertido en el mecanismo judicial más utilizado por las personas para lograr la eficaz protección de sus derechos fundamentales recuperándose así la confianza en los operadores judiciales por lo tanto se requiere enriquecer su aspecto técnico y jurídico en los estudiantes. Este documento académico trata de los siguientes temas especializados de la acción de tutela: marco normativo, definición, derechos que se protegen, presupuestos procesales, competencia, caducidad, conflicto de competencias, medidas provisionales, flujograma procedimental y virtual, además de los aspectos novedosos de la acción de tutela contra providencias judiciales y tutela contra tutela. En conclusión, debido a que la acción de tutela se ha convertido en un mecanismo prevalente y a que se usan las tecnologías de la información y las comunicaciones en las actuaciones judiciales (justicia virtual) en virtud del decreto 806 del 2020, se hace indispensable ofrecer una presentación de la acción de tutela y de sus presupuestos técnicos y procesales requeridos actualmente. Solo así se obtiene una formación integral de los estudiantes y cumplir con la competencia del curso de acciones constitucionales de proponer soluciones a problemas jurídicos para la defensa de los intereses individuales y colectivos a partir de la aplicación del marco constitucional colombiano.
Bringing Genetics to the Public: Personalized Genomic Medicine and You Podcast
Authors: Holland Isabella Rose
Published: April 2023

The topics of genetic testing and genomic medicine may sound complicated, but they are becoming increasingly relevant in our lives. From direct-to-consumer testing services like 23andMe to clinical tests that help people make medical decisions, genetic tests are becoming more prevalent than ever. Understanding how they are used, how they are interpreted, and how to find available resources is important, but it can be difficult to find information about these topics if you don’t know where to look. The goal of this podcast episode is to describe the world of personalized genomic medicine through interviews with genetic professionals, including a researcher at the Iowa Institute for Human Genetics and a practicing genetic counselor. The Personalized Genomic Medicine and You podcast provides listeners with a foundational background of how genetic testing is utilized in today’s medical landscape. This knowledge can allow patients and consumers alike to make informed testing decisions in both clinical and personal contexts.For those interested in the previous episode of Personalized Genomic Medicine and You, I interviewed Dr. Adela Mansilla, a researcher at the Iowa Institute of Human Genetics, about the basics of personalized genomic medicine and genetic testing. The first episode of this podcast series can be found on Spotify: https://open.spotify.com/episode/1Nh88JOi1rb7FYtgFDolz8?si=cb017328b9164a8f.
Association of SOD2 Polymorphism (Rs4880) and Competitive Performance in Track and Field Athletes
Authors: KAZAN Hasan Huseyin , BULĞAY Celal , ZORBA Erdal , BAYRAKTAR Işık , CERİT Mesut , ERGUN Mehmet Ali
Published: April 2023

The rs4880 polymorphism in the SOD2 gene has frequently been associated with the athletic performance of the athletes in different populations. However, there were not any studies in the Turkish elite athletes. Thus, the aim of the study is to evaluate the impacts of SOD2 rs4880 polymorphism on competitive performance of Turkish elite athletes. A total of 60 elite athletes (31 sprint/power and 29 endurance) and 20 controls with the ages of 18-35 voluntarily participated in the study. World Athletics (WA) score table was used to determine the performance levels of the competitive performance/personal best (PB) of the athletes. Whole exome sequencing (WES) was performed by the genomic DNA isolated from peripheral blood of the participants. Sport type, sex and competitive performance were chosen as the parameters to compare within and between the groups by linear regression models. There were not any significances between the genotype and allele frequencies. Finally, there were not any associations between competitive performance and genotype frequencies within the sprint/power athletes while competitive performance was significantly (p=0.049) correlated with rs4880 polymorphism according to the over-dominant model within the endurance athletes. The rs4880 polymorphism in the SOD2 gene may be linked to the competitive performance of the endurance athletes. Still, further studies with more participants and analyzing multiple factors are needed.
​​Irritating Bowels​: Attention and Everyday Management of Gut Trouble in Denmark
Authors: Laursen Camilla Brændstrup
Published: April 2023

Irritable bowel syndrome (IBS) constitutes an irritating and embarrassing problem for an estimated 11–16% of the Danish population. Based on long-term ethnographic fieldwork, this article explores how young and middle-aged people diagnosed with IBS attend to, experience, and manage gut trouble in a Danish welfare context. It asks how we may understand the relation between IBS, irritation, and attention. Drawing on conceptualisations of ‘dys-appearance’ (Leder 1990) and ‘attentional pulls’ (Throop and Duranti 2015), I explore how afflicted individuals’ attention is pulled towards unwanted and unexpected gut sensations in everyday life, and how a Danish welfare context, manifesting itself in notions of ‘faring well’ (Langer and Højlund 2011) and moral imaginings of ‘good lives’ (Mattingly 2014), may contribute to this. Furthermore, I show how people are impelled to experiment with consciously paying attention to the gut and deciphering its signals to try to alleviate gut trouble. I suggest that irritation may not only be an empirical focal point, but also a heuristic tool for troubling and refining concepts.
Distribution and Some Diagnostic Properties of Capoeta damascina (Valenciennes, 1842) in Streams of the Ceyhan and Seyhan River Basins, Türkiye
Authors: KARA Cemil , ALP Ahmet , BOZALİ Nuri
Published: April 2023

This study determined the distribution and some diagnostic characteristics of Capoeta damascina, which belongs to the Cyprinidae family, in the Ceyhan and Seyhan River basins. C. damascina individuals were detected in 21 of 25 Ceyhan River Basin stations and 12 out of 15 in the Seyhan River basin stations. C. damascina show a wide distribution in the Ceyhan and Seyhan River basins. It has been determined that C. damascina lives at altitudes between 125 m (Hemite Stream) and 1620 m (Söğütlü Stream) in the Ceyhan River basins, and in streams at altitudes ranging from 165 m (Çakıt stream) to 1758 m (Sarız Stream) in the Seyhan River basins. The average total length of C. damascina individuals (n: 218) caught in the Ceyhan River Basin is 14.72 cm, and their average weight is 40.12 g. In the Seyhan River basin, the average total length of C. damascina individuals (n: 74) was found to be 17.31 cm, and their average weight was 72.24 g. The mean number of line lateral scales was 71, the number of gill rakers was 23, and the number of pharyngeal teeth was 4:3:2-2:3:4.
Biomass to hydrogen: A short biomass potential availability and conversion survey for Dutch municipalities
Authors: Rob Bastiaans
Published: April 2023

The local availability of lignocellulosic biomass was estimated for an arbitrary but representative Dutch municipality to facilitate the conversion and use of hydrogen on a decentralized scale. This study reveals that there is a large potential for this biomass at many locations to play important roles in the energy transition. It is meant as a call to the scientific community to activate individuals and get some inspiration.
Volume of oils (fatty acids) in coffee and comparison between roast levels v1
Authors: not provided Shannon.logan
Published: April 2023

Measuring the volume of oils produced in coffee across a range of roasts.
Washington University SenNet Case Collection Protocol v1
Authors: Rapp Daniel
Published: April 2023

This document outlines inclusion and exclusion criteria for healthy participants for the WUSTL Senescence Network (SenNet) by the Tissue Mapping Center (TMC) at Washington University.
WUSTL Senescence Network (SenNet) Biospecimen Collection SOP v1
Authors: Rapp Daniel
Published: April 2023

This protocol describes the tissue collection and storing aspect of Washington University's Biospecimen core as part of the Cellular Senescence Network (SenNet).
Association of the metabolic syndrome components with the chronotype, level of daytime sleepiness and dispositional optimism in patients with arterial hypertension
Authors: Novak-Mazepa Khrystyna , Marushchak Mariya , Tomchuk Tetiana , Gashynska Olena , Pastryk Tetiana
Published: April 2023

Introduction: Arterial hypertension (AH) coexists with such conditions as obesity, insulin resistance/hyperinsulinemia and dyslipidemia, that is, interrelated metabolic disorders that characterize the metabolic syndrome (MS). The aim of the study was to establish relationships between components of MS and chronotype, level of daytime sleepiness, and dispositional optimism in patients with AH.
Methods: The study included 42 patients diagnosed with stage 2 essential AH. To study the chronotype, we used the validated questionnaire “Composite Scale of Morningness” and scales from official available sources. Dispositional optimism (LOT-R) was assessed using a questionnaire adapted from O.A. Sychova. Epworth scale was used to assess the severity day sleepiness. Laboratory parameters were determined using commercially available kits.
Results: The main components of the MS are found in more than half of the patients with AH, of which 61.90% have abdominal obesity, 42.86% have hypertriacylglycerolemia, 26.19% have hyperglycemia, and 66.67% have a decrease in HDL-C levels. Patients with AH with diagnosed components of MS have an evening chronotype, daytime sleepiness, and low optimism is diagnosed to the same extent as patients without MS. At the same time, evening (53.85%) and intermediate (38.46%) chronotypes are found in patients with abdominal obesity, and the frequency of daytime sleepiness registration, and a low level of optimism does not depend on the presence of abdominal obesity; patients with dyslipidemia are characterized by low optimism, evening and intermediate chronotypes, and severe daytime sleepiness; and a glucose level ≥5.6 mmol/l is registered in most patients with an evening chronotype.
Conclusion: The research conducted showed significant relationships between abdominal obesity, dyslipidemia, hyperglycemia and evening chronotype, daytime sleepiness, and low optimism in patients with AH.
Smart Engagement and Smart Urbanism: Integrating “The Smart” Into Participatory Planning and Community Engagement
Authors: Jung Jin-Kyu , Kang Jung Eun
Published: April 2023

The smart city epitomizes a new paradigm shift in urban planning, policy, and cities. Smart cities require and are powered by smart city principles to succeed, including smart technologies, smart infrastructure, and smart governance; however, they also need to engage closely with the citizens who are most affected by the deployment of the smart city and who also embrace the diverse perspectives, experiences, and opportunities of living in smart cities, i.e., smart engagement. What would be forms of collaborative democracy and inclusive citizen participation in smart city planning? To what extent can smart city planning respond and address inequality, justice, and social and digital division? How can we create community-based climate change planning with the smart? What would be a smart community platform that supports smart engagement, and how do cities around the world establish smart city policy and assess the impact on smart engagement? This thematic issue aims to answer these questions by exploring new visions, facets and methods, practices, and tools for enabling smart engagement. Drawing on research from various countries and cities across the world, the contributions bring new prospects of smart engagement and smart urbanism and illuminate how the theory, plan and policy, and practices of smart engagements are binding to the extent of citizen participation and engagement in smart cities.
Load Balancing in Distributed Cloud Computing: A Reinforcement Learning Algorithms in Heterogeneous Environment
Authors: Shahakar Minal , Mahajan Surenda , Patil Lalit
Published: April 2023

Balancing load in cloud based is an important aspect that plays a vital role in order to achieve sharing of load between different types of resources such as virtual machines that lay on servers, storage in the form of hard drives and servers. Reinforcement learning approaches can be adopted with cloud computing to achieve quality of service factors such as minimized cost and response time, increased throughput, fault tolerance and utilization of all available resources in the network, thus increasing system performance. Reinforcement Learning based approaches result in making effective resource utilization by selecting the best suitable processor for task execution with minimum makespan. Since in the earlier related work done on sharing of load, there are limited reinforcement learning based approaches. However this paper, focuses on the importance of RL based approaches for achieving balanced load in the area of distributed cloud computing. A Reinforcement Learning framework is proposed and implemented for execution of tasks in heterogeneous environments, particularly, Least Load Balancing (LLB) and Booster Reinforcement Controller (BRC) Load Balancing. With the help of reinforcement learning approaches an optimal result is achieved for load sharing and task allocation. In this RL based framework processor workload is taken as an input. In this paper, the results of proposed RL based approaches have been evaluated for cost and makespan and are compared with existing load balancing techniques for task execution and resource utilization..
Condition based Ensemble Deep Learning and Machine Learning Classification Technique for Integrated Potential Fishing Zone Future Forecasting
Authors: Vinston Raja R. , Ashok Kumar K. , Gokula Krishnan V.
Published: April 2023

Artificial Intelligence (AI) technologies have become a popular application in order to improve the sustainability of smart fisheries. Although the ultimate objective of AI applications is often described as sustainability, there is yet no proof as to how AI contributes to sustainable fisheries. The proper monitoring of the longitudinal delivery of different human impacts on activities such as fishing is a major concern today in aquatic conservation. The term "potential fishing zone" (PFZ) refers to an anticipated area of any given sea where a variety of fish may congregate for some time. The forecast is made based on factors including the sea surface temperature (SST) and the sea superficial chlorophyll attentiveness. Fishing advisories are a by-product of the identification procedure. Normalization and preliminary processing are applied to these unprocessed data. The gathered attributes, together with financial derivatives and geometric features, are then utilised to make projections about IPFZ's Technique are used to get the final determination (CECT). In this study, we offer a technique for identifying and mapping fishing activity. Experimentations are performed to validate the efficacy of the CECT method in comparison to machine learning (ML) and deep learning (DL) methods across a variety of measurable parameters. Results showed that CECT obtained 94% accuracy, while Convolutional neural network only managed 92% accuracy on 80% training data and 20% testing data.
Role of Digitalization in Election Voting Through Industry 4.0 Enabling Technologies
Authors: Vaqur Musheer , Kumar Rajesh , Singh Rajesh , Umang Umang , Gehlot Anita , Vaseem Akram Shaik , Joshi Kapil
Published: April 2023

The election voting system is one of the essential pillars of democracy to elect the representative for ruling the country. In the election voting system, there are multiple areas such as detection of fake voters, illegal activities for fake voting, booth capturing, ballot monitoring, etc., in which Industry 4.0 can be adopted for the application of real-time monitoring, intelligent detection, enhancing security and transparency of voting and other data during the voting. According to previous research, there are no studies that have presented the significance of industry 4.0 technologies for improving the electronic voting system from a sustainability standpoint. To overcome the research gap, this study aims to present literature about Industry 4.0 technologies on the election voting system. We examined individual industry enabling technologies such as blockchain, artificial intelligence (AI), cloud computing, and the Internet of Things (IoT) that have the potential to strengthen the infrastructure of the election voting system. Based upon the analysis, the study has discussed and recommended suggestions for the future scope such as: IoT and cloud computing-based automatic systems for the detection of fake voters and updating voter attendance after the verification of the voter identity; AI-based illegal, and fake voting activities detection through vision node; blockchain-inspired system for the data integrity in between voter and election commission and robotic assistance system for guiding the voter and also for detecting disputes in the premises of election booth.
A Novel Machine learning Algorithms used to Detect Credit Card Fraud Transactions
Authors: Sudhakar M. , Kaliyamurthie K. P.
Published: April 2023

During the Covid-19 pandemic, the world was under lockdown, and everyone was inside their home. There are so many restrictions for going out, so many companies introduced online shopping, and this online shopping helped more people; the e-commerce platform also increased their revenue; at the same time, online fraud has also risen worldwide. Everyone adopted online shopping during the pandemic. In 2019 India's 2019 credit/debit card fraud rate was 365, according to the National Crime Record Bureau. The developed countries are the highest rate of credit card fraud in 2020 compared to India; for that reason, we have to implement mechanisms that can detect credit theft. The machine learning algorithm with the R program will play an essential role in credit card fraud detection. The following machine learning algorithm will have used for credit card fraud, Random Forest, Logistic regression, Decision trees, and Gradient Boosting Classifiers. The European bank dataset used in our research and the dataset size is 284808. Here we used two classes, the first one is called the positive class (fraud transactions), and the second one is the negative class (genuine transactions). The final result will show us the outperforms of our proposed system.
An Adaptable Optimal Network Topology Model for Efficient Data Centre Design in Storage Area Networks
Authors: Rajan Sanju , Joseph Linda
Published: April 2023

In this research, we look at how different network topologies affect the energy consumption of modular data centre (DC) setups. We use a combined-input directed approach to assess the benefits of rack-scale and pod-scale fragmentation across a variety of electrical, optoelectronic, and composite network architectures in comparison to a conventional DC. When the optical transport architecture is implemented and the appropriate resource components are distributed, the findings reveal fragmentation at the layer level is adequate, even compared to a pod-scale DC. Composable DCs can operate at peak efficiency because of the optical network topology. Logical separation of conventional DC servers across an optical network architecture is also investigated in this article. When compared to physical decentralisation at the rack size, logical decomposition of data centers inside each rack offers a small decrease in the overall DC energy usage thanks to better resource needs allocation. This allows for a flexible, composable architecture that can accommodate performance based in-memory applications. Moreover, we look at the state of fundamentalmodel and its use in both static and dynamic data centres. According to our findings, typical DCs become more energy efficient when workload modularity increases, although excessive resource use still exists. By enabling optimal resource use and energy savings, disaggregation and micro-services were able to reduce the typical DC's up to 30%. Furthermore, we offer a heuristic to duplicate the Mixed integer model's output trends for energy-efficient allocation of caseloads in modularized DCs.
An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM
Authors: Rajesh P. , Kavitha R.
Published: April 2023

Deep learning (DL) algorithms have substantially increased research in recognizing day-to-day human activities All methods for recognizing human activities that are found through DL methods will only be useful if they work better in real-time applications.  Activities of elderly people need to be monitored to detect any abnormalities in their health and to suggest healthy life style based on their day to day activities. Most of the existing approaches used videos, static photographs for recognizing the activities. Those methods make the individual to feel anxious that they are being monitored. To address this limitation we utilized the cognitive outcomes of DL algorithms and used sensor data as input to the proposed model which is collected from smart home dataset for recognizing elderly people activity, without any interference in their privacy. At early stages human activities the input for human activity recognition by DL models are done using single sensor data which are static and lack in recognizing dynamic and multi sensor data. We propose a DL architecture based on the blend of deep Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) in this research which replaces human intervention by automatically extracting features from multifunctional sensing devices to reliably recognize the activities. During the entire investigation process we utilized Tulum, a benchmark dataset that contains the logs of sensor data. We exhibit that our methodology outperforms by marking its accuracy as 98.76% and F1 score as 0.98.
Enhancing Flight Delay Prediction through Feature Engineering in Machine Learning Classifiers: A Real Time Data Streams Case Study
Authors: Jadhav Shailaja B. , Kodavade D. V.
Published: April 2023

The process of creating and selecting features from raw data to enhance the accuracy of machine learning models is referred to as feature engineering. In the context of real-time data streams, feature engineering becomes particularly important because the data is constantly changing and the model must be able to adapt quickly. A case study of using feature engineering in a flight information system is described in this paper. We used feature engineering to improve the performance of machine learning classifiers for predicting flight delays and describe various techniques for extracting and constructing features from the raw data, including time-based features, trend-based features, and error-based features. Before applying these techniques, we applied feature pre-processing techniques, including the CTAO algorithm for feature pre-processing, followed by the SCSO (Sand cat swarm optimization) algorithm for feature extraction and the Enhanced harmony search for feature optimization. The resultant feature set contained the 9 most relevant features for deciding whether a flight would be delayed or not. Additionally, we evaluate the performance of various classifiers using these engineered features and contrast the results with those obtained using raw features. The results show that feature engineering significantly improves the performance of the classifiers and allows for more accurate prediction of flight delays in real-time.
Improving Accuracy of Integrated Neuro-Fuzzy Classifier with FCM based Clustering for Diagnosis of Psychiatric Disorder
Authors: Singh Chandrabhan , Gangwar Mohit , Kumar Upendra
Published: April 2023

Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Autism spectrum disorder (ASD) is a neurodevelopment disorder. Clinical decision-making process is complex. Due to complex nature of disease sign and its symptoms clinical decision making may lead to misclassification. To deal with such complex medical problems methods or approaches of soft computing play an important role. This paper will focus on presenting an integrated Neuro-fuzzy model. This integrated model has the learning strength of neural network and knowledge representation ability of fuzzy logic. Modified Adaptive Neuro –Fuzzy inference system (M-ANFIS) is used here for classification and predication. Here Fuzzy C-mean (FCM) Clustering is used first to make classes of data before presenting in to ANFIS. This FCM based class will reduce the classifier computational overhead. Precision error and recall, F-measure and accuracy matrices are used to compare the experimental results with other classic methods.
Automatic Human Face Detection and Recognition Based On Facial Features Using Deep Learning Approach
Authors: Jatain Rashmi , Jailia Manisha
Published: April 2023

In recent years, there has been an increased emphasis placed on the identification of face traits in studies. The human face is the most significant characteristic that may be used in the process of identifying a person. Even the most genetically identical twins may be distinguished from one another by a few key facial characteristics. Therefore, to discern one from the other, a human face identification and detection system that is based on facial traits is necessary. This study suggests a technique for automated human face identification and recognition based on facial characteristics that are achieved via the use of deep learning. It would seem that deep learning, with its high rate of accuracy, would be an appropriate method to use while carrying out face recognition. Face detection and identification may be accomplished using a process known as deep learning. According to the results of the study, it is possible to conclude that the approach that was suggested is superior to other ways in terms of accuracy, precision, recall, and f1-score.
A Survey on an Effective Identification and Analysis for Brain Tumour Diagnosis using Machine Learning Technique
Authors: Parshapa Padma , Rani P. Ithaya
Published: April 2023

The hottest issue in medicine is image analysis. It has drawn a lot of researchers since it can effectively assess the severity of the condition and forecast the outcome. The noise trimming outcomes, on the other hand, have reduced with more complex trained images, which has tended to result in a lower prediction exactness score. So, a novel Machine Learning prediction framework has been built in this present study. This work also tries to predict brain tumours and evaluate their severity using MRI brain scans. Using the boosting function, the best results for error pruning are produced. The Proposed Solution function was then used to successfully complete the feature analysis and tumour prediction operations. The intended framework is evaluated in the Python environment, and a comparative analysis is performed to examine the prediction improvement score. It was discovered that an original MLPM model had the best tumour prediction precision.
Design of a Web Portal for Assertive Diagnosis and Cognitive Evolution in Children with Special Abilities
Authors: Huamaní Enrique Lee , Meneses-Claudio Brian , Velarde-Molina Jehovanni Fabricio , Mirji Hema , Orellano-Benancio Lizbardo
Published: April 2023

A world beyond the five senses is what opens the doors to new expectations for children with special abilities, as they lack a variety of factors due to a prejudiced society with middle-aged life stereotypes. We will use the SCRUM method to make the appropriate changes in a short time this established to norms and laws both in education and health, what we develop is a web application that generates a diagnosis for cognitive evolution in children with special abilities. The number of special children who tried the software have made great strides in terms of individual performance. The studies carried out serve the teachers as health professionals who attend to the child with a report, in order to also contribute to the individual and social improvement that the children with special abilities will present through the therapies they need.
BLA2C2: Design of a Novel Blockchain-based Light-Weight Authentication & Access Control Layer for Cloud Deployments
Authors: Khare Surbhi , Badholia Abhishek
Published: April 2023

Cloud deployments are consistently under attack, from both internal and external adversaries. These attacks include, but are not limited to brute force, masquerading, improper access, session hijacking, cross site scripting (XSS), etc. To mitigate these attacks, a wide variety of authentication & access control models are proposed by researchers, and each of them vary in terms of their internal implementation characteristics. It was observed that these models are either highly complex, or lack in terms of security under multiple attacks, which limits their applicability for real-time deployments. Moreover, some of these models are not flexible and cannot be deployed under dynamic cloud scenarios (like constant reconfigurations of Virtual Machines, dynamic authentication use-cases, etc.). To overcome these issues, this text proposes design of a novel blockchain-based Light-weight authentication & access control layer that can be used for dynamic cloud deployments. The proposed model initially applies a header-level light-weight sanitization layer that removes Cross Site Scripting, SQL Injection, and other data-level attacks. This is followed by a light-weight authentication layer, that assists in improving login-level security for external attacks. The authentication layer uses IP matching with reverse geolocation mapping in order to estimate outlier login attempts. This layer is cascaded with an efficient blockchain-based access control model, which assists in mitigating session hijacking, masquerading, sybil and other control-level attacks. The blockchain model is developed via integration of Grey Wolf Optimization (GWO) to reduce unnecessary complexities, and provides faster response when compared with existing blockchain-based security deployments. Efficiency of the model was estimated in terms of accuracy of detection for different attack types, delay needed for detection of these attacks, and computational complexity during attack mitigation operations. This performance was compared with existing models, and it was observed that the proposed model showcases 8.3% higher accuracy, with 10.5% lower delay, and 5.9% lower complexity w.r.t. standard blockchain-based & other security models. Due to these enhancements, the proposed model was capable of deployment for a wide variety of large-scale scenarios.
Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods
Authors: Manikandan G , Hung Bui Thanh , S Siva Shankar , Chakrabarti Prasun
Published: April 2023

Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models.
An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection
Authors: Shelar Yogita , Sharma Prashant , Rawat Chandan Singh. D.
Published: April 2023

As the field of image processing and computer vision continues to develop, we are able to create edited images that seem more natural than ever before. Identifying real photos from fakes has become a formidable obstacle. Image forgery has become more common as the multimedia capabilities of personal computers have developed over the previous several years. This is due to the fact that it is simpler to produce fake images. Since image object fabrication might obscure critical evidence, techniques for detecting it have been intensively investigated for quite some time. The publicly available datasets are insufficient to deal with these problems adequately. Our work recommends using a deep learning based image inpainting technique to create a model to detect fabricated images. To further detect copy-move forgeries in images, we use an CNN-LSTM and Improved VGG adaptation network. Our approach could be useful in cases when classifying the data is impossible. In contrast, researchers seldom use deep learning theory, preferring instead to depend on tried-and-true techniques like image processing and classifiers. In this article, we recommend the CNN-LSTM and improved VGG-16 convolutional neural network for intra-frame forensic analysis of altered images.
Conversational AI for Natural Language Processing: An Review of ChatGPT
Authors: Goar Vishal , Yadav Nagendra Singh , Yadav Pallavi Singh
Published: April 2023

ChatGPT is a conversational artificial intelligence model developed by OpenAI, which was introduced in 2019. It employs a transformer-based neural mesh to produce human being responses in real-time, allowing for natural language conversations with a machine. ChatGPT is instructed on huge quantities of data captured using the internet, making it knowledgeable in an extensive span of topics, from news & entertainment to politics and sports. This allows it to generate contextually relevant responses to questions and statements, making the conversation seem more lifelike. The model can be used in various applications, including customer service, personal assistants, and virtual assistants. ChatGPT has also shown promising results in generating creative content, such as jokes and poetry, showcasing its versatility and potential for future applications.This paper provides a comprehensive review of the existing literature on ChatGPT, highlighting its key advantages, such as improved accuracy and flexibility compared to traditional NLP tools, as well as its limitations and the need for further research to address potential ethical concerns. The review also highlights the potential for ChatGPT to be used in NLP applications, including question-answering and dialogue generation, and highlights the need for further research and development in these areas.