bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Performance of IoT Networks in Acoustic Environment by using LZW Data Compression Technique
Authors: Sisodia Ankur , Yadav Ajay Kumar
Published: September 2023

The Internet of Things (IoT) has experienced phenomenal growth, opening up a wide range of applications in many settings. Due to the properties of sound propagation, IoT networks operating in acoustic environments in particular present special difficulties. Data compression techniques can be used to minimize overhead and maximize resource utilization in these networks to increase performance. The performance of IoT networks in acoustic environments is examined in this study, with a focus on routing overhead, throughput, and typical end-to-end delay. Lempel-Ziv-Welch (LZW) data compression is used to reduce data size and boost communication effectiveness. Three well-known protocols—MQTT, CoAP, and Machine-to-Machine (M2M)—are assessed in relation to acoustic Internet of Things networks. To mimic different acoustic conditions and collect performance metrics, a thorough experimental setup is used. Different network topologies, data speeds, and compression settings are used in the studies to determine how they affect the performance metrics. According to the analysis's findings, all three protocols' routing overhead is greatly decreased by the LZW data compression approach, which enhances network scalability and lowers energy usage. Additionally, the compressed data size has a positive impact on network throughput, allowing for effective data transmission in acoustic contexts with limited resources. Additionally, using LZW compression is seen to minimize the average end-to-end delay, improving real-time communication applications. This study advances knowledge of IoT networks operating in acoustic environments and the effects of data reduction methods on their functionality. The results offer useful information for network engineers and system designers to optimize the performance of IoT networks in similar situations. Additionally, a comparison of the MQTT, CoAP, and M2M protocols' suitability for acoustic IoT deployments is provided, assisting in the choice of protocol for particular application needs.
A Hybridized- Logistic Regression and Deep Learning-based Approaches for Precise Anomaly Detection in Cloud
Authors: Rajarao Banavathu , Sreenivasulu Meruva
Published: September 2023

Anomaly Detection plays a pivot role in determining the abnormal behaviour in the cloud domain. The objective of the manuscript is to present two approaches for Precise Anomaly Detection Approaches by hybridizing RBM with LR and SVM models. The various phases in the present approach are (a) Data collection (b) Pre-processing and normalization; OneHot Encoder for converting categorical values to numerical values followed by encoding the binary features through normalization (c) training the data (d) Building the Feedforward Deep Belief Network (EDBN) using hybridizing Restricted Boltzmann Machine (RBM) with Logistic Regression (LR) and Support Vector Machine (SVM); In the first approach, RBM model is trained through unsupervised pre-training followed by fine-tuning using LR model. In the later approach, RBM model is trained through unsupervised pre-training followed by fine-tuning using SVM model; both the approaches adopt unsupervised pre-training followed by supervised-fine-tuning operations (e) Model Evaluation using the significant parameters such as Precision, Recall, Accuracy, F1-score and Confusion Matrix. The experimental evaluations concluded the effective anomaly detection techniques by integrating the RBM with LR and SVM for capturing the intricate patterns and complex relationships among the data. The proposed approaches paves a path to improved anomaly detection technique, thereby enhancing the security features and anomaly monitoring systems across distinct domains.
Design and Demonstration of Arduino based Low Cost Pulse Oximeter
Authors: Vananth P. N. Shakthi , Rajalakshmi S. , Sathya P.
Published: September 2023

One of the main reasons for the pushback of the pandemic in India was the awareness that developed in people during the crisis. The adapting mindset of the Indian populace helped turn the tides. The Pulse Oximeter proved to be an important tool that helped in identifying the affected, to get timely healthcare. A very simple information was shared among people that the BPM (heart rate in beats per minute) should be around 60 - 100 (resting) for an average adult and the SpO2, i.e., oxygen saturation must be above 95% for all age  groups. Just this was able to counter an entire wave of the Pandemic with the help of early detections, timely isolations, immediate health care and much more. However, access to this was not available to everyone due to the high prices of these devices. This project aims to build a simple monitor module with an Arduino Nano board which can determine the Blood Oxygen level and Heart rate of a person by using the MAX30102 sensor.
Crude Oil Cost Forecasting using Variants of Recurrent Neural Network
Authors: Zaware Sarika , Kanawade Bhavana , Pimpalkar Shilpa , Chetan Aher , Phapale Anuja , Zaware Sumedha
Published: September 2023

Crude oil cost plays very important role in the country’s economic growth. It is  having close impact on economical stability of nation. Because of these reasons it is very important to have accurate oil forecasting system. Due to impact of different factors oil cost data is highly nonlinear and in fluctuated manner. Performing prediction on those data using data driven approaches is very complex task which require lots of preprocessing of data. Working on such a non-stationary data is very difficult. This research proposes recurrent neural network (RNN) based approaches such as simple RNN, deep RNN and RNN with LSTM. To compare performance of RNN variants this research has also implemented Naive forecast and Sequential ANN methods. Performance of all these models are evaluated based on root mean square error(RMSE), mean absolute error(MAE) and mean absolute percentage error(MAPE). The experimental result shows that RNN with LSTM is more accurate compare to all other models. Accuracy of LSTM is more than 96% for the dataset of U.S. Energy Information administration from March 1983 to June 2022. On the basis of experimental result, we come to the conclusion that RNN with LSTM is best suitable for time series data which is highly nonlinear.
Assessment of Security Trepidation in Cloud Applications with Enhanced Encryption Algorithms
Authors: Srilekha Thotakuri , S Vijay Bhanu , P Niranjan
Published: September 2023

To alleviate crank in routine process in IT related work environment we are maintaining information’s in cloud storage even though we affected by pandemic and other natural disaster still can able to access data by avoiding degrade in target process. Members who posses account in cloud no need to have separate high end configuration devices because even less configured devices could connect to cloud and make use of all services using virtual machine. Applications belong to cloud storage intimidated in the aspect of safety. This paper reviews the various security related issues and its causes along with latest cloud security attacks. We discussed about different technology to protect information resides in cloud and analyzed different enhanced algorithm for encryption for securing the data in cloud due to surge use of devices interacting cloud services.
An Optimized AMS Based Cloud Downloading Service with Advanced Caching and Intelligent Data Distribution Mechanism
Authors: Prasanna S. , Shanthini B.
Published: September 2023

The popularity of peer-to-peer video content downloading has surged due to diverse content availability and convenient sharing among users. However, scaling systems to accommodate the growing number of users and content items poses a challenge. This research aims to optimize video content downloading in peer-to-peer systems. The objective is to improve performance by developing advanced caching mechanisms, an intelligent data distribution algorithm, and efficient bandwidth resource management. The proposed approach involves implementing innovative caching mechanisms that store frequently accessed content closer to users, reducing download time. An intelligent data distribution algorithm minimizes bottlenecks and maximizes download speeds. Efficient bandwidth resource management ensures fair allocation. Results demonstrate significant enhancements in download time and overall system performance, leading to improved user experience. This research addresses the need for an optimized video content downloading system to handle increasing user and content volumes. The findings hold the potential to enhance user experiences, facilitate seamless video sharing, and advance peer-to-peer video content downloading.
Use of Key Points and Transfer Learning Techniques in Recognition of Handedness Indian Sign Language
Authors: Ingoley Shilpa N. , Bakal Jagdish W.
Published: September 2023

The most expressive way of communication for individuals who have trouble speaking or hearing is sign language. Normal people are unable to comprehend sign language. As a result, communication barriers are put up. Majority of people are right-handed. Statistics say that, an average population of left-handed person in the world is about 10%, where they use left hand as their dominating hand. In case of hand written text recognition, if the text is written by left-handed or right-handed person, then there would not be any problem in recognition neither for human and nor for computer. But same thing is not true for sign language and its detection using computer. When the detection is performed using computer vision and if it falls into the category of detection by appearance, then it might not detect correctly. In machine and deep learning, if the model is trained using just one dominating hand, let’s say right hand, then the predictions can go wrong if same sign is performed by left-handed person. This paper addresses this issue. It takes into account the signs performed by any type of signer: left-handed, right-handed or ambidexter. In proposed work is on Indian Sign Language (ISL). Two models are trained: Model I, is trained on one dominating hand and Model II, is trained on both the hands. Model II gives correct predictions regardless of any type of signer. It recognizes alphabets and numbers in ISL. We used the concept of Key points and Transfer Learning techniques for implementation. Using this approach, models get trained quickly and we could achieve validation accuracy of 99%.
DFQIoV: Design of a Dynamic Fan-Shaped-Clustering Model for QoS-aware Routing in IoV Networks
Authors: Suryawanshi Sadaf Ravindra , Gupta Praveen
Published: September 2023

Internet of Vehicles (IoV) is a steadily growing field of research that deals with highly ad-hoc wireless networks. These networks require design of high-speed & high-efficiency routing models, that can be applied to dynamically changing network scenarios. Existing models that perform this task are highly complex and require larger delays for estimation of dynamic routes. While, models that have faster performance, do not consider comprehensive parameters, which limits their applicability when used for large-scale network scenarios. To overcome these limitations, this text proposes design of a novel dynamic fan-shaped clustering model for QoS-aware routing in IoV networks. The model initially collects network information sets including node positions, & energy levels, and combines them with their temporal packet delivery & throughput performance levels. These aggregated information sets are processed via a hybrid bioinspired fan shaped clustering model, that aims at optimization of routing performance via deployment of dynamic clustering process. The model performs destination-aware routing process which assists in reducing communication redundances. This is done via a combination of Elephant Herding Optimization (EHO) with Particle Swarm Optimization (PSO), which integrates continuous learning for router level operations. The integrated model is able to reduce communication delays by 5.9%, while improving energy efficiency by 8.3%, throughput by 4.5%, and packet delivery performance by 1.4% under different network scenarios. Due to which the proposed model is capable of deployment for a wide variety of dynamic network scenarios.
Monkeypox Detection Through Watershed Segmentation and Appending 2D CNN Based Auto Encoder
Authors: T Krishnan , K Selvakumar , S Vairachilai
Published: September 2023

Monkeypox, a viral zoonosis, may spread from animals to people. Fever, rashes, and swollen lymph nodes might create medical complications. Its symptoms resemble smallpox. To prevent monkey pox sickness, you must be prepared and treat it immediately. Public health systems should be aware of effective monkeypox mitigation methods because to its global health impacts. Watershed segmentation using CNN-based auto encoder detected monkeypox. Monkeypox may be distinguished from other skin infections. Watershed segmentation, elevation map utilisingsobel, and region-based feature extraction function well on impacted skin photos. Segmenting Monekypox images is tough due to similarities and variations across classes and the difficulties of focusing on skin lesions. Unsupervised learning models like the convolutional autoencoder duplicate the input image in the output layer. Encoders, ConvNets that produce low-dimensional images, process images passed via them.
EHFT: An Ensembled Hyperopt Fine-Tuned Neural Network for Disease Detection in Tomato Plants
Authors: Padmanabhuni Sri Silpa , Gera Pradeepini
Published: September 2023

The identification of unhealthy plants in the crops at the early stage of cultivation helps for good farming. Unhealthy parts can be recognized using shape, color and texture, which are processed using feature extraction techniques. The feature extraction system stores the images in the matrix pixel format, which requires 3 channels for processing the images. Traditional neural networks utilize backpropagation techniques to adjust the random weights, which requires many resources while extracting a more significant number of features from a huge amount of data. These mechanisms also require more trainable parameters during the transformation of data from one layer to another. The proposed model implements the pre-trained model "RESNET152" (Residual Network), which is efficient for feature extraction and designs the last layer of the network as a "Tuned X-Gradient Boosting" ensemble algorithm for performing the binary classification of tomato leaves. RESNET can reduce computational resources because it implements residual blocks which fasten the learning rate by skipping a few connections in the network. The fine-tuned ensemble model helps the model identify the best parameters quickly. The learnable parameters are the essential elements of any ML model because they can easily identify the patterns associated with the different features. In the proposed model for feature extraction, pattern matching is the crucial step. Therefore, it is very necessary to tune the   XGBOOST algorithm. Compared to the traditional approaches, the proposed model enhanced the accuracy performance in training and testing with 98.58% and 95.56%, correspondingly
Performance Rubrics for Robustness Evaluation of Web Mutation Operators
Authors: Mallika S. Suguna , Lakshmi D. Rajya , Rani T. Esther
Published: September 2023

Web Applications are the predominant medium for not only business enterprises but also for service-based sector to establish and continue their online presence. However, the robustness of web application is mandatory in seamless interaction with customers for achieving sustainable business. Intruders and unethical hackers keep trying to gain unauthentic access to the web applications and hence it is more necessary for the web application to be resistant against any such attacks. The strength of a web application is indirectly responsible for gaining customer confidence leading to repeat business as well as attracting new customers for profitable longer run. Once the web application gains credibility it is bound to run successfully. In the current work, an attempt has been made to assess the robustness of mutation operators used to test web applications is made. A few rubrics have been proposed to ascertain the strength of projected mutation operators verified on some sample open-source web applications. The functional attributes of a web application are the functionalities offered by the web application. The non-functional attributes of a typical web application are security, performance, availability. Here, web applications are challenged against the afore mentioned non-functional attributes using rubrics like uniformity, uniqueness, reliability, unpredictability, and entropy. A comprehensive analysis has been made for the robustness of the projected web operators against the designed and formulated rubrics.
Revolutionizing Healthcare through Health Monitoring Applications with Wearable Biomedical Devices
Authors: Alegavi Sujata S. , Nemade Bhushankumar , Bharadi Vinayak , Gupta Shiwani , Singh Vidyadhari , Belge Archana
Published: September 2023

The Internet of Things (IoT) has revolutionized the connectivity and communication of tangible objects, and it serves as a versatile and cost-effective solution in the healthcare sector, particularly in regions with limited healthcare infrastructure. This research explores the application of sensors such as LM35, AD8232, and MAX30100 for the detection of vital health indicators, including body temperature, pulse rate, electrocardiogram (ECG), and oxygen saturation levels, with data transmission through IoT cloud, offering real-time parameter access via an Android application for non-invasive remote patient monitoring. The study aims to expand healthcare services to various settings, such as hospitals, commercial areas, educational institutions, workplaces, and residential neighborhoods. After the COVID-19 pandemic, IoT-enabled continuous monitoring of critical health metrics such as temperature and pulse rate has become increasingly crucial for early illness detection and efficient communication with healthcare providers. Our low-cost wearable device, which includes ECG monitoring, aims to bridge the accessibility gap for people with limited financial resources, with the primary goal of providing efficient healthcare solutions to underserved rural areas while also contributing valuable data to future medical research. Our proposed system is a low-cost, high-efficiency solution that outperforms existing systems in healthcare data collection and patient monitoring. It improves access to vital health data and shows economic benefits, indicating a significant advancement in healthcare technology.
Novel Proposed Work for Empirical Word Searching in Cloud Environment
Authors: Priya K. Hema , Kumar M. Senthil , Gobinath T. , Kumar M. , Sathishkumar A. , Shankar C. Udhaya
Published: September 2023

People's lives have become much more convenient as a result of the development of cloud storage. The third-party server has received a lot of data from many people and businesses for storage. Therefore, it is necessary to ensure that the user's data is protected from prying eyes. In the cloud environment, searchable encryption technology is used to protect user information when retrieving data. The versatility of the scheme is, however, constrained by the fact that the majority of them only offer single-keyword searches and do not permit file changes.A novel empirical multi-keyword search in the cloud environment technique is offered as a solution to these issues. Additionally, it prevents the involvement of a third party in the transaction between data holder and user and guarantees integrity. Our system achieves authenticity at the data storage stage by numbering the files, verifying that the user receives a complete ciphertext. Our technique outperforms previous analogous schemes in terms of security and performance and is resistant to inside keyword guessing attacks.The server cannot detect if the same set of keywords is being looked for by several queries because our system generates randomized search queries. Both the number of keywords in a search query and the number of keywords in an encrypted document can be hidden. Our searchable encryption method is effective and protected from the adaptive chosen keywords threat at the same time.
MRI Kidney Tumor Image Classification with SMOTE Preprocessing and SIFT-tSNE Features using CNN
Authors: S Raghavi , P Naveen , R Ranjith , S Vinu , Mohanaprakash T. A. , S Vinod Kumar
Published: September 2023

Kidney tumor detection is a challenging task due to the complexity of tumor characteristics and variability in imaging modalities. In this paper, we propose a deep learning-based approach for detecting kidney tumors with 98.5% accuracy. Our method addresses the issue of an imbalanced dataset by applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the distribution of images. SMOTE generates synthetic samples of the minority class to increase the number of samples, thus providing a balanced dataset. We utilize a convolutional neural network (CNN) architecture that is trained on this balanced dataset of kidney tumor images. The CNN can learn and extract relevant features from the images, resulting in precise tumor classification. We evaluated our approach on a separate dataset and compared it with state-of-the-art methods. The results demonstrate that our method not only outperforms other methods but also shows robustness in detecting kidney tumors with a high degree of accuracy. By enabling early detection and diagnosis of kidney tumors, our proposed method can potentially improve patient outcomes. Additionally, addressing the imbalance in the dataset using SMOTE demonstrates the usefulness of this technique in improving the performance of deep learning-based image classification systems.
Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN
Authors: Sree P. Hema , Nagajyothi D. , Revathi K. , Rao B. Janardhana
Published: September 2023

Image degradation plays a major problem in many image processing applications. Due to blurring, the quality of an image is degraded and there will be a reduction in bandwidth. Blur in an image is due to variations in atmospheric turbulence, focal length, camera settings, etc. Various types of blurs include Gaussian blur, Motion blur, Out-of-focus blur. The effect of noise along with blur further corrupts the captured image. Many techniques have evolved to deblur the degraded image. The leading approach to solve various degraded images are either based on discriminative learning models or on optimization models. Each method has its own advantages and disadvantages.  Learning by discriminative methods is faster but restricted to a specific task whereas optimization models handle flexibly but consume more time. Integrating optimization models suitably by learning with discriminative manner results in effective image restoration. In this paper, a set of effective and fast Convolutional Neural Networks (CNNs) are employed to deblur the Gaussian, motion and out-of-focus blurred images that integrate with optimization models to further avoid noise effects. The proposed methods work more efficiently for applications with low-level vision.
SVMDnet: A Novel Framework for Elderly Activity Recognition based on Transfer Learning
Authors: Nagpal Diana , Gupta Shikha
Published: September 2023

Elderly Activity Recognition has become very crucial now-a-days because majority of elderly people are living alone and are vulnerable. Despite the fact that several researchers employ ML (machine learning) and DL (deep learning) techniques to recognize elderly actions, relatively lesser research specifically aimed on transfer learning based elderly activity recognition. Even transfer learning is not sufficient to handle the complexity levels in the HAR related problems because it is a more general approach. A novel transfer leaning based framework SVMDnet is proposed in which pre-trained deep neural network extracts essential action features and to classify actions, Support Vector Machine (SVM) is used as a classifier. The proposed model is evaluated on Stanford-40 Dataset and self-made dataset. The older volunteers over the age of 60 were recruited for the main dataset, which was compiled from their responses in a uniform environment with 10 kinds of activities. Results from SVMDnet on the two datasets shows that our model behaves well with human recognition and human-object interactions as well.
Predictive Analytics in Healthcare: Empowering Consultation with Machine Learning
Authors: More Priyanka , Sakhare Sachin , Gupta Manya , Singh Kirti , Gavhane Om , Mantri Rushikesh
Published: September 2023

The Smart Healthcare and Online Consultation initiative intends to offer patients a quick and convenient online platform for seeking medical advice and services. Real-time video consultations, appointment scheduling, prescription administration, and health records management are just a few of the capabilities available on the platform. To deliver individualized and superior healthcare services, the initiative to use cutting-edge such as AI, ML, and data analytics. By giving patients an easy and affordable way to receive healthcare services remotely, the Smart Healthcare and Online Consultation initiative has the potential to completely transform the healthcare sector.
Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches
Authors: Mandal Akshaya Kumar , Sarma Pankaj Kumar Deva , Dehuri Satchidananda
Published: September 2023

A self-learning disease detection model will be useful for identifying skin infections in suspected individuals using skin images of infected patients. To detect skin diseases, some AI-based bioinspired models employ skin images. Skin infection is a common problem that is currently faced due to various reasons, such as food, water, environmental factors, and many others. Skin infections such as psoriasis, skin cancer, monkeypox, and tomato flu, among others, have a lower death rate but a significant impact on quality of life. Neural Networks (NNs) and Swarm intelligence (SI) based approaches are employed for skin disease diagnosis and classification through image processing. In this paper, the convolutional neural networks-based Cuckoo search algorithm (CNN-CS) is trained using the well-known multi-objective optimization technique cuckoo search. The performance of the suggested CNN-CS model is evaluated by comparing it with three commonly used metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO. This comparison was based on various measures, including accuracy, precision, recall, and F1-score. These measures are calculated using the confusion matrices from the testing phase. The results of the experiments revealed that the proposed model has outperformed the others, achieving an accuracy of 97.72%.
A LONGITUDINAL APPROACH TO ONLINE “COLLECTIVE IDENTITY WORK”: THE CASE OF THE GILETS JAUNES IN THE VAR DEPARTMENT*
Authors: Morselli Davide , Beramendi Maite , Bendali Zakaria , Fillieule Olivier
Published: September 2023

Social movement theorists have highlighted the importance of accounting for the fluidity of collective identities and the ways in which they change over time. Capitalizing on the availability of social media data and the shift from collective to connective action, new methods can be used to model identity change over a medium-to-long time span. We analyze Facebook data to make the case that a complex and longitudinal approach to the study of collective identity is not only possible but also necessary. Our analyses explore the formation and transformation of identity in the context of a local branch of the Gilets Jaunes (Yellow Vests) movement in France. By employing a mixed-method design that combines automated topic modeling, content analysis, and dictionary-based linguistic inquiry, we show how collective identities are discussed through complex and conflictual processes that form perpetual identity work.
Caracterização Microclimática do Complexo Esportivo Arena Pantanal
Authors: Fank Naiara Cristina , Novais Jonathan Willian Zangeski , Hillesheim Ana Cristina
Published: September 2023

O uso e ocupação do solo a fim de solucionar as demandas físicas e sociais das cidades alteraram a paisagem urbana e degradam o meio ambiente. A retirada da vegetação nativa, o aumento no número de edificações e o aumento das superfícies impermeáveis, contribuem para a elevação da temperatura, acarretando o aumento do desconforto térmico. O Complexo Esportivo Arena Pantanal é uma das áreas urbanas mais importantes da cidade de Cuiabá-MT, com fins esportivos e de recreação para população, seu entorno é caracterizado por pouca arborização e muitas áreas impermeabilizadas. Neste contexto, o objetivo geral deste trabalho é a caracterização microclimática do entorno da Arena Pantanal de acordo com os dados coletados de temperatura do ar e umidade relativa do ar, realizados nos períodos quente/seco e quente/úmido, em 12 pontos distintos do complexo esportivo e em três horários do dia, às 8:00h, às 14:00h e às 20:00h. É indicado nos resultados que ocorre no período quente/úmido os maiores valores de temperatura do ar e umidade relativa do ar em comparação com a estação quente e seca, e que o tipo de material que reveste o solo interfere nas variáveis supracitadas, podendo aumentar o desconforto térmico dos usuários de áreas urbanas.
 
Palavras-chave: Temperatura do Ar. Umidade Relativa do Ar. Microclima Urbano.
 
Abstract
The use and occupation of land in order to solve the physical and social demands of cities altered the urban landscape and degraded the environment. The removal of native vegetation, the increase in the number of buildings and the increase in impermeable surfaces contribute to the rise in temperature, causing an increase in thermal discomfort. The Pantanal Arena Sports Complex is one of the most important urban areas in the city of Cuiabá, with sports and recreation purposes for the population, its surroundings are characterized by little afforestation and many waterproofed areas. In this context, the general objective of this work is the microclimatic characterization of the surroundings of the Arena Pantanal according to the collected data of air temperature and relative humidity, carried out in the hot/dry and hot/humid periods, in 12 different points of the complex. sports and at three times of the day, at 8:00h, 14:00h and 20:00h. The results indicated that the hot/humid period presented air temperature and relative humidity with higher values ​​compared to the hot and dry season, and that the type of material that covers the soil also interferes in the variables of air temperature and relative humidity, which may increase the thermal discomfort of users in urban areas.
 
Keywords: Air Temperature. Relative Humidity. Urban Microclimate.
Efeitos da Elevação da Saturação por Bases no Crescimento Inicial de Mudas de Ipê Amarelo
Authors: Costa Jorge Willian da , Costa Patrícia Paz da , Botelho Rosangela Araujo , Vieira Cristiane Ramos
Published: September 2023

A qualidade é um fator que influenciará no tempo de crescimento das mudas no viveiro e, posteriormente, no campo. Um dos requisitos para aumentar a qualidade das mudas, é o substrato, que pode ter suas características químicas melhoradas com a aplicação do calcário. Diante disso, o presente estudo teve por objetivo avaliar os efeitos da saturação por bases no crescimento inicial de mudas de ipê amarelo (Handroanthus chrysotrichus), em condições de viveiro e, posteriormente, recomendar a saturação mais adequada. Para isso, as mudas de ipê amarelo foram produzidas em sacolas plásticas e, ao atingirem 10 cm de altura, aplicou-se o calcário. Os tratamentos testados foram: T0 – nível atual de V% do solo; T1 – 50% saturação por bases; T2 - 60% saturação por bases; T3 - 70% saturação por bases; T4 - 80% saturação por bases; T5 - 90% saturação por bases. Utilizando-se delineamento inteiramente casualizado, com seis repetições. Ao final de 90 dias, foram medidas as características morfológicas, obtendo-se: incremento em altura e em diâmetro; massa seca aérea; massa seca radicular; massa seca total; relação altura/diâmetro; relação massa aérea/massa radicular e; índice de qualidade de Dickson. Para elevar a saturação por bases, foi recomendado o tratamento de 50%, porque auxiliou na obtenção de mudas de ipê amarelo de qualidade.
 
Palavras-chave: Handroanthus chrysotrichus. Calagem. Produção de Mudas. Viveiro.
 
Abstract
Quality is a factor that will influence the growth time of seedlings in the nursery and, later, in the field. One of the requirements to increase the quality of seedlings is the substrate, which can have its chemical characteristics improved with the application of limestone. Therefore, this study aimed to evaluate the effects of base saturation on the initial growth of yellow ipe seedlings (Handroanthus chrysotrichus) under nursery conditions and, subsequently, to recommend the most appropriate saturation. For this, the yellow ipe seedlings were produced in plastic bags and, when they reached 10 cm in height, limestone was applied. The treatments tested were: T0 – current level of V% of the soil; T1 – 50% base saturation; T2 - 60% base saturation; T3 - 70% base saturation; T4 - 80% base saturation; T5 - 90% base saturation. Using a completely randomized design, with six replications. At the end of 90 days, the morphological characteristics were measured and obtained: increase in height and diameter; aerial dry mass; root dry mass; total dry mass; height/diameter ratio; aerial mass/root mass ratio and; Dickson quality index. To increase the base saturation, the treatment of 50% was recommended, because it helped to obtain quality yellow ipe seedlings.
 
Keywords: Handroanthus chrysotrichus. Liming. Seedlings Production. Nursery.
Frequência de Anticorpo Anti-Neospora caninum em Cães do Município de Seropédica, Estado do Rio de Janeiro
Authors: Dias Cordeiro Matheus , Rosa dos Santos Leila Maria , Nascimento Furtado de Freitas Aline , Barbosa da Silva Jenevaldo , Henrique da Fonseca Adivaldo , De Azevedo Baêta Bruna
Published: September 2023

Neospora caninum é um protozoário de grande importância, responsável por causar neuropatias em canídeos e abortamento em bovinos. A prevalência de anticorpos anti-N. caninum é consideravelmente diferente entre países e entre as regiões geográficas do Brasil, apresentando uma ampla variação de soropositividade entre hospedeiros definitivos e intermediários. Neste sentido, o estudo objetivou avaliar a frequência de anticorpos anti-N. caninum em cães do município de Seropédica, Rio de Janeiro, a fim de obter informações relativas aos cães residentes de uma área periurbana. Foram coletadas amostras sanguíneas de 271 cães com diferentes idades, ambos os sexos e predominantemente sem raça definida. Para detecção de anticorpos IgG anti-N. caninum foi realizado o ensaio de imunoabsorção enzimática indireto (ELISA indireto) in house a partir do soro desses animais. A prevalência geral de anticorpos anti-N. caninum encontrada nos cães do município de Seropédica foi de 37,64% (102/271). Nenhuma variável analisada (sexo, raça, idade, acesso a rua, acesso à pasto ou curral e soropositividade para Toxoplama gondii) apresentou-se estatisticamente associada (p>0,05) com a soropositividade dos animais. O presente estudo indica a presença de anti-N. caninum em cães e serve para evidenciar a necessidade de criação de um programa de controle da infecção para cães nessa área periurbana.
Palavras-chave: Neosporose. Sorologia. Cães. Periurbana
AbstractNeospora caninum is a protozoan of great importance, responsible for causing neuropathies in canids and abortion in cattle. The prevalence of anti-N. caninum antibodies is considerably different between countries and between geographic regions of Brazil, with a wide range of seropositivity between definitive and intermediate hosts. Therefore, the study aimed to evaluate the frequency of anti-N. caninum in dogs from the municipality of Seropédica, state of Rio de Janeiro, in order to obtain information regarding dogs resident in a peri-urban area. Blood samples were collected from 271 dogs of different ages, both sexes and predominantly mixed breed. In order to detect anti-N. caninum IgG antibodies, an indirect enzyme immunosorbent assay (indirect ELISA) was performed in house from the serum of these animals. The general prevalence of anti-N. caninum antibodies found in dogs in the municipality of Seropédica was 37.64% (102/271). No variable analyzed (sex, race, age, access to the street, access to pasture or corral and seropositivity for Toxoplama gondii) was statistically associated (p >0.05) with the animals' seropositivity. The present study indicates the presence of anti-N. caninum in dogs, and should serve to highlight the need to create an infection control program for dogs in this peri-urban area.
Keywords: Neosporosis. Serology. Dogs. Periurban Area
Soybean Seeds Submitted to Different Water Deficit Periods after Sowing in Dry Soil
Authors: Santos Cassiano Vasconcelos dos , Nunes Ubirajara Russi , Menegaes Janine Farias , Nied Astor Henrique , Ludwig Eduardo José , Zini Priscila Barbieri , Bastiani Géssica Gabordi de , Bernardt Eduardo
Published: September 2023

Abstract
Sowing in dry soil aims to alleviate the effects of water restriction, allowing for an earlier start of sowing and maximizing operational productivity in the use of agricultural machinery and implements. Thus, the objective of the present work was to evaluate the physiological quality of soybean seed lots submitted to different periods of water deficit after sowing in dry soil under vegetal cover. The experimental design was completely randomized, in a 2x5x2 factorial scheme (seed lots: high and low vigor; periods of water deficit after sowing in dry soil: 0; 4; 8; 12 and 16 days after sowing (DAS) and soil cover crops: without and with), with four replications. The evaluations were carried out from emergence to the beginning of the crop's reproductive phase. It was verified that there was no triple interaction of the factorial, where each factor individually interfered on the physiological quality of soybean seeds, regardless of their initial vigor. It was concluded that the use of plots with different vigor affects negatively the emergence of soybean seedlings, as well as the different periods of water deficit after sowing in dry soil. However, the presence of vegetation cover on the soil mitigates this effect, allowing greater emergence due to this promoting more favorable environmental conditions.
Keywords: Glycine max. Vegetation Ground Cover. Seed Vigor.
ResumoA semeadura em solo seco visa amenizar os efeitos de restrição hídrica, permitindo o início da semeadura mais precoce e a maximização da produtividade operacional no uso de máquinas e implementos agrícolas. Deste modo, o objetivo do presente trabalho foi avaliar a qualidade fisiológica de lotes de sementes de soja submetidas aos diferentes períodos de déficit hídrico após a semeadura no solo seco sob cobertura vegetal do solo. O delineamento experimental foi conduzido inteiramente casualizado, no esquema fatorial de 2x5x2 (lotes de sementes: alto e baixo vigor; períodos de déficit hídrico após a semeadura no solo seco: 0; 4; 8; 12 e 16 dias após a semeadura (DAS) e coberturas vegetais do solo: sem e com), com quatro repetições. As avaliações foram realizadas a partir da emergência até o início da fase reprodutiva da cultura. Verificou-se que não houve interação tripla do fatorial, onde cada fator individualmente teve interferência sobre a qualidade fisiológica das sementes de soja, independente do seu vigor inicial. Conclui-se que a utilização de lotes com diferença de vigor afeta negativamente a emergência das plântulas de soja, bem como os diferentes períodos de déficit hídrico após a semeadura no solo seco. Todavia, a presença de cobertura vegetal sobre o solo ameniza esse efeito, possibilitando maior emergência em virtude dessa promover condições ambientais mais favoráveis.
Palavras-chaves: Glycine max. Cobertura Vegetal do Solo. Vigor de Sementes.
Animales, delincuentes y menores. Un análisis sobre la circulación de estigmas sociales en un grupo de seguridad de Facebook de la Ciudad Autónoma de Buenos Aires (2017-2019)
Authors: Dikenstein Violeta
Published: September 2023

In this article, we examine the narratives that circulate in a Facebook group about the problem of crime and insecurity in Buenos Aires during the years 2017 and 2019. Specifically, we consider the ways in which members of this group typify criminals and theorize about the characteristics of crime. Through the qualitative analysis of a survey on the posts and interactions of a group in this social media, we observe the coexistence —without contradiction— of blasphemies and the allusion to killing the criminals with well-intentioned and supportive publications about abandoned animals. The former is the target of the group’s stigmatizing exercise, while the latter appear as the representation of kindness and tenderness. Hence, we observe a marked selective sensibility, which points out who deserves to live and which lives are less worthy of being preserved.
TREATMENT OF CONGENITAL DUODENAL OBSTRUCTION: FROM OPEN TO LAPAROSCOPIC APPROACH FOR DUODENO-DUODENAL ANASTOMOSIS.
Authors: Shchapov Nikolay , Ekimovskaya Ekaterina , Kulikov Denis , Mayorov Alexey , Shatova Svetlana , Sergeyeva Svetlana
Published: September 2023

Conginental duodenal obstruction is a relatively rare malformation. Laparoscopic operation for this condition was described for the first time in 2001, but more than 20 years later, there is still debate over the preferred method of surgical correction. We believe that laparoscopic correction of conginental duodenal obstruction is a safe and feasible method and can be used in premature infants with low body weight. Materials and Methods: From September 2017 to December 2021, 27 children with conginental duodenal obstruction were treated in our department. We were able to identify the diagnosis in 17 children during the antenatal period, while plain X-ray confirmed postnatal diagnosis. In doubtful cases the contrast fluoroscopy was performed. Four children underwent open correction of the defect via a circumbilical approach, while laparoscopic duodeno-duodenal anastomosis using the Kimura technique was performed in the remaining 23 children. Results: Intraoperative complication in the form of duct injury was observed in 1 patient with an atypically located Wirsung duct. In the postoperative period, anastomotic failure was noted in 2 children, and perforation of the duodenum was detected in 2 patients. The mortality rate comprised 26%, which was partly attributed to severe concomitant pathologies. The use of prolonged epidural analgesia in combination with laparoscopic surgery provided early weaning from mechanical ventilation and transfer from the intensive care unit. Enteral feeding was initiated on postoperative day 5, and the average length of hospital stay was 29±10.5 days. Conclusion: There are no limitations to performing laparoscopic correction of congenital duodenal obstruction. When there are accompanying congenital defects, minimally invasive technology allows to perform combined operations on organs of the thoracic and abdominal cavity, which reduces the overall surgical time. In combination with prolonged epidural analgesia, laparoscopic technique reduces the length of stay in the intensive care unit and the need for parenteral nutrition, which ultimately lowers the risk of inflammatory complications and treatment costs.
Determination of serotype distribution of aggregatibacter actinomycetemcomitans and its relationship to Herpes virus in patients with aggressive periodontitis
Authors: Murugappan Subbulakshmi
Published: September 2023

Aggregatibacter actinomycetemcomitans (A. a) and its serotypes were commonly associated with Aggressive Periodontitis (AgP). Herpes virus along with A.a in sites of destruction, suggested that their co-occurence reduces the ability of macrophages to respond to bacteria and inhibits their phagocytic activity. JP2 clone is a highly leukotoxic strain of A.a, associated with Keywincreased progression of periodontal disease.

The aim of the study was to determine the serotype distribution of A.a and its relationship to the presence of Herpes virus [Epstein Barr virus (EBV) and Cytomegalovirus (CMV)] in patients with AgP.

The study included 20 patients with AgP and 20 periodontally healthy subjects. Subgingival plaque samples were collected from deepest pockets using Gracey curettes and transferred to transport media. The cDNA was extracted and analysed by PCR.

Serotype d and JP2 were found to be significantly more prevalent among the participants and showed a positive correlation with increased PD and CAL, suggesting an association with the severity of AgP. Though CMV and EBV were also found to be associated with AgP, the results were insignificant. Females showed more association with disease severity than males, but the results were insignificant.

There was a positive correlation between serotype d and JP2 with the periodontal status. CMV and EBV were also associated with the severity of AgP, but the results were insignificant. Further quantitative studies should prove the exact role of co- occurrence of serotypes with CMV and EBV and their effect on AgP.
The conglomeration of NEP, UGC, and accreditation guidelines - The way ahead
Authors: Manivasakan Shivasakthy
Published: September 2023

This article aims to discuss the conglomeration of the National Education Policy 2020 with the University Grants Commission and the other accreditation guidelines that are already existing and the directions for the future. It is neither for nor against the policy and doesn’t express any political views.

National Education Policy released in 2020 has been discussed a lot in many forums, media, and publications. Anything introduced new would always have to face lots of critiques and challenges before it gets implemented. The governance of universities and institutions have become more formal and standardized with enormous documentation procedure. The release of the National Education Policy invoked mixed feelings amongst the academic administrators and those working on the accreditation side, as they envisioned more challenges and documentation to be faced in the future.

The parameters of the National Education Policy were compared with the parameters of NIRF, NAAC, and UGC guidelines.

The conglomeration of the policy documents discussed in this article concludes that most of the points of the national education policy are already being followed by the institutions and it’s not completely new to be perceived as a huge challenge.