bluuebunny/crossref_metadata_2025 huggingface dataset:
Interlined dynamic voltage restorer using time-domain methodologies with Z-source inverter/voltage source inverter
Authors: Anuradha Chandrasekar , Raman Rathinam Anantha , Arunsankar Ganesan , Joel Maharajan Robinson , Sathyanathan Pitchamuthu , Kantari Hanumaji , Murugan Subbiah
Published: July 2024

Electronic devices and loads are very sensitive to the voltage disturbances like voltage sag and voltage swell. Significant financial losses and safety issues may emerge from voltage sags and interruptions, which can be caused by variables such as system breakdowns and load changes. In order to protect against voltage fluctuations and keep vital loads running, dynamic voltage restorers (DVRs) have become more popular. To mitigate the voltage disturbances, an interlined DVR (IDVR) using a Z-source inverter (ZSI) is developed to protect the sensitive devices and loads. Back-to-back DVR connects the distributed feeders with a common direct current (DC) link. The IDVR compensates for the sag voltage and supplies the energy to control the power flow. In addition, proposed a modified synchronous reference frame (MSRF)/direct quadrature theory, hysteresis controller, and proportional integral (PI) controller, which provides the required amount of control signals for a ZSI and voltage source inverter (VSI). MATLAB/Simulink validated the simulation results. The experimental findings show that the suggested system can be implemented successfully and is effective at reducing voltage dips and interruptions, allowing crucial loads to keep operating consistently and without interruption in residential as well as commercial environments.
Comparative study of wind turbine emulator control using an asynchronous motor: IRFOC and DTC
Authors: Zekraoui Hana , Ouchbel Taoufik , El Hafyani Mohamed Larbi
Published: July 2024

This work is an overview of one of the renewable energy sources, wind power. The high cost of testing wind turbines and analyzing their characteristics in research laboratories prompted us to create the Wind Emulator. In this article, we will proceed with the development of an emulator of a wind turbine conversion chain based on an asynchronous machine. This emulator would be capable of faithfully reproducing the dynamic characteristics of a real wind turbine and of integrating them optimally into a real electrical system so as to be able to study its operation at laboratory level, so our objective is to have an emulator that will provide the characteristics (speed-torque-current) in real time and with realistic conditions. Our development approach is based on the use of two classical control strategies under the MATLAB/Simulink closed-loop environment: direct torque control (DTC) and indirect rotor flux vector control (IRFOC) in dynamic and static regimes. The simulation results presented and discussed in this work enable us to determine the operating limits of our proposed wind emulator, in order to validate the most suitable emulator model. Ultimately, this model will be integrated into an intelligent computing board such as the DSP1104.
The research on the signal source number estimation algorithm
Authors: Peizhi Wang , Mohamed Raihani , Mustapha Norwati , Manshor Noridayu
Published: July 2024

In array signal processing, Estimating the quantity of signal sources represents a crucial area of investigation. In this paper, a comprehensive introduction and analysis of the estimation methods for determining the number of signal sources are presented, including the background and significance, and the significance of precise estimation of the quantity of signal sources. The influence of factors such as signal-to-noise ratio (SNR), noise background, and number of snapshots on the estimation algorithm is discussed in detail. At the same time, common array models are introduced. Then, different signal source number estimation algorithms are analyzed in detail, and their respective advantages and applicable conditions are pointed out. Finally, the performance of each algorithm in different situations is evaluated by comparing the performance of the algorithms under different SNRs, snapshot numbers, and array elements. The experimental results show that with the increase of the SNR and the number of array elements, the correct estimation probability of the algorithm also increases correspondingly, which provides a reliable experimental basis and performance evaluation for the estimation.
Histopathological cancer detection based on deep learning and stain images
Authors: Ibrahim Dina M. , Hammoudeh Mohammad Ali A. , Allam Tahani M.
Published: July 2024

Colorectal cancer (CRC)-a malignant growth in the colon or rectum- is the second largest cause of cancer deaths worldwide. Early detection may increase therapy choices. Deep learning can improve early medical detection to reduce the risk of unintentional death from an incorrect clinical diagnosis. Histopathological examination of colon cancer is essential in medical research. This paper proposes a deep learning-based colon cancer detection method using stain-normalized images. We use deep learning methods to improve detection accuracy and efficiency. Our solution normalizes image stain variations and uses deep learning models for reliable classification. This research improves colon cancer histopathology analysis, which may enhance diagnosis. Our paper uses DenseNet-121, VGG-16, GoogLeNet, ResNet-50, and ResNet-18 deep learning models. We also analyze how stain normalization (SN) improves our model on histopathology images. The ResNet-50 model with SN yields the highest values (9.94%) compared to the other four models and the nine models from previous studies.
Tailoring AES for resource-constrained IoT devices
Authors: Saleh Shaimaa S. , Al-Awamry Amr A. , Taha Ahmed
Published: July 2024

The internet of things (IoT) is a network of interconnected hardware, software, and many infrastructures that require cryptography solutions to provide security. IoT security is a critical concern, and it can be settled by using cryptographic algorithms such as advanced encryption standard (AES) for encryption and authentication. A fundamental component within the AES algorithm is the substitution box (S-box), which generates confusion and nonlinearity between plaintext and ciphertext, strengthening the process of security. This paper introduces a comparative analysis to offer valuable knowledge of the factors related to different S-box modifications, which will ultimately affect the design of cryptographic systems that use the AES algorithm. Then, a tailored AES algorithm is proposed for resource-constrained IoT devices by changing the standard S-box with another S-box. The new S-box reduces the rounds number and the time needed for the AES algorithm’s encryption, decryption, and key expansion. The performance of the proposed AES is assessed through various experiments. Therefore, our tailored AES with the new S-box is more secure and efficient than AES with a standard S-box.
Towards robust security in WSN: a comprehensive analytical review and future research directions
Authors: Zhukabayeva Tamara , Zholshiyeva Lazzat , Ven-Tsen Khu , Mardenov Yerik , Adamova Aigul , Karabayev Nurdaulet , Abdildayeva Assel , Baumuratova Dilaram
Published: July 2024

One of the most important aspects of the effective functioning of wireless sensor network (WSN) is their security. Despite significant progress in WSN security, there are still several unresolved issues. Many review studies have been published on the problems of possible attacks on WSN and their identification. However, due to the lack of their systematic analysis, it is not possible to fully substantiate practical recommendations for the effective application of the proposed solutions in the field of WSN security. In particular, the creation of methods that provide a high degree of security while minimizing computational effort and costs, and the development of effective methods for detecting and preventing attacks on WSN. The purpose of this document is to fill this gap. The article presents the results of the study in the form of a systematic analysis of the literature with a targeted selection of sources to identify the most effective methods for detecting and preventing attacks on WSN. By identifying the security of WSN, which has not yet been addressed in research works, the review aims to reduce its impact. As a result, our extended taxonomy is presented, including attack types, datasets, effective WSN attack detection methods, countermeasures, and intrusion detection systems (IDS).
Development of an algorithm for integrated UAV groups using visible light communication technology
Authors: Alibekkyzy Karygash , Keribayeva Talshyn , Koshekov Kayrat , Baidildina Aizhan , Bugubayeva Alina , Azamatova Zhanerke
Published: July 2024

Our research group dedicated its idea in developing and analyzing an algorithm for transforming integrated unmanned aerial vehicle (UAV) groups (IUGs) using visible light communication (VLC) technology. This innovative approach is designed to enhance UAV network coordination, addressing the complex challenges of communication within these networks. The primary issue addressed is the pressing need for advanced communication mechanisms within UAV networks to ensure efficient. This is a robust data transfer and complex coordination between UAVs. The existing systems lack the required adaptability and efficiency, leading to operational inefficiencies and reduced effectiveness in UAV applications. The main results of the study are concluded in the design and implementation of the conversion algorithm. Which provides efficient and reliable data transmission and sophisticated coordination between UAVs. Through careful mathematical modeling of UAV group dynamics and extensive MATLAB simulations, the study demonstrates the algorithm's ability to effectively control UAV formations. This method gives adaptability to different operational requirements and supports collision-free maneuvers. The algorithm's innovative design and the comprehensive approach adopted in the study, including the use of VLC technology and the integration of advanced restructuring methods, enable the effective resolution of the identified communication challenges within UAV networks.
Optimizing dialog policy with large action spaces using deep reinforcement learning
Authors: Thakkar Manisha , Pise Nitin
Published: July 2024

Dialogue policy is responsible to select the next appropriate action from the current dialogue state to accomplish the user goal efficiently. Present commercial task-oriented dialogue systems are mostly rule-based; thus, they are not easily scalable to adapt multiple domains. To design an adaptive dialogue policy, user feedback is an essential parameter. Recently, deep reinforcement learning algorithms have been popularly applied to such problems. However, managing large state-action space is time consuming and computationally expensive. Additionally, it requires good quality and a reliable user simulator to train the dialogue policy which takes additional design efforts. In this paper, we propose a novel approach to improve the performance of dialogue policy by accelerating the training process by using imitation learning for deep reinforcement learning. We utilized proximal policy optimization (PPO) algorithm to model dialogue policy using a large-scale multi-domain tourist dataset MultiWOZ2.1. We observed a remarkable performance of dialogue policy with 91.8% task success rate, and an approximate 50% decrease in the average number of turns required to complete tasks without using user simulator in the early phase of training cycles. This approach is expected to help researchers to design computationally efficient and scalable dialogue agents by avoiding training from scratch.
An evaluation of multiple classifiers for traffic congestion prediction in Jordan
Authors: Hassan Mohammad , Arabiat Areen
Published: July 2024

This study contributes to the growing body of literature on traffic congestion prediction using machine learning (ML) techniques. By evaluating multiple classifiers and selecting the most appropriate one for predicting traffic congestion, this research provides valuable insights for urban planners and policymakers seeking to optimize traffic flow and reduce jamming and. Traffic jamming is a global issue that wastes time, pollutes the environment, and increases fuel usage. The purpose of this project is to forecast traffic congestion at One of the most congested areas in Amman city using multiple ML classifiers. The Naïve Bayes (NB), stochastic gradient descent (SGD) fuzzy unordered rule induction algorithm (FURIA), logistic regression (LR), decision tree (DT), random forest (RF), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with our study area. These will be assessed by accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments show that FURIA is the classifier that presents the highest predictions of traffic congestion where By 100% achieved Accuracy, Precision, Sensitivity and F-measure. In the future further studies can be used more datasets and variables such as weather conditions; and drivers behavior that could integrated to predict traffic congestion accurately.
Customized convolutional neural networks for Moroccan traffic signs classification
Authors: Khalloufi Fatima Ezzahra , Rafalia Najat , Abouchabaka Jaafar
Published: July 2024

Recognition of traffic signs is a challenging task that can enhance road safety. Deep neural networks have demonstrated remarkable results in numerous applications, such as traffic signs recognition. In this paper, we propose an innovative and efficient system for recognizing traffic signs, based on customized convolutional neural network (CNN) developed through hyperparameters optimization. The effectiveness of the proposed system is assessed using a novel dataset, the Moroccan traffic signs dataset. The results show that the proposed design recognizes traffic signs with an accuracy of 0.9898, outperforming several CNN architectures such as VGGNet, DensNet, and ResNet.
Improved deep learning architecture for skin cancer classification
Authors: Owida Hamza Abu , Alshdaifat Nawaf , Almaghthawi Ahmed , Abuowaida Suhaila , Aburomman Ahmad , Al-Momani Adai , Arabiat Mohammad , Chan Huah Yong
Published: July 2024

A leading cause of mortality globally, skin cancer is deadly. Early skin cancer diagnosis reduces mortality. Visual inspection is the main skin cancer diagnosis tool; however, it is imprecise. Researchers propose deep-learning techniques to assist physicians identify skin tumors fast and correctly. Deep convolutional neural networks (CNNs) can identify distinct objects in complex tasks. We train a CNN on photos with merely pixels and illness labels to classify skin lesions. We train on HAM-10000 using a CNN. On the HAM10000 dataset, the suggested model scored 95.23% efficiency, 95.30% sensitivity, and 95.91% specificity.
Eye disease detection using transfer learning based on retinal fundus image data
Authors: Imaduddin Helmi , Sakina Alivia Rahma
Published: July 2024

The escalating global prevalence of blindness remains a pressing concern, with eye diseases representing the primary culprits behind this issue. Vision is integral to various aspects of human life, underscoring the significance of effective eye disease detection. Presently, disease detection relies largely on manual methods, which are susceptible to misdiagnosis. However, the advent of technology has paved the way for disease detection through the application of deep learning methodologies. Deep learning exhibits substantial potential in disease detection, particularly when applied to image data, as attested by its accuracy in algorithmic assessments. This research introduces a novel approach to disease detection, specifically transfer learning-based deep learning. The study seeks to evaluate and compare the performance of various models, including EfficientNetB3, DenseNet-121, VGG-16, and ResNet-152, in identifying three prevalent eye diseases: cataract, diabetic retinopathy, and glaucoma, utilizing retinal fundus image data. Extensive experimentation reveals that the DenseNet-121 model achieves the highest accuracy levels, boasting precision, recall, F1-score, and accuracy values of 96.5%, 96%, 96.25%, and 96.20%, respectively. These results demonstrate the superior performance of the employed transfer learning model, signifying its efficacy in detecting eye diseases.
Exploring the tree algorithms to generate the optimal detection system of students' stress levels
Authors: Yamasari Yuni , Qoiriah Anita , Rochmawati Naim , Prapanca Aditya , Prihanto Agus , Suartana I Made , Ahmad Tohari
Published: July 2024

The significant changes in the world of education after the coronavirus disease 2019 (COVID-19) pandemic have increased students' anxiety levels. This anxiety can trigger stress which can interfere with students' academic performance. Therefore, this condition is a critical problem that needs to be addressed immediately. However, researchers have not previously conducted much research to detect post-COVID stress levels. Apart from that, the existence of a system capable of carrying out this detection is still lacking. Therefore, this research focuses on building a system for detecting student stress levels. First, an exploration of the tree algorithm was carried out to find the most optimal method for recognizing student stress levels. Then a detection system is built using this optimal method. The research results show that the tree ID3 (Iterative Dichotomiser 3) algorithm achieves the highest accuracy value of 95% compared to other tree algorithms with the scenario of dividing training data into test data of 80%:20%. Moreover, this telegram bot-based detection system works well in recognizing three categories of stress, namely: light-, moderate-, and heavy stress based on black-box testing techniques.
User self-efficacy enhances business intelligence tools for organizational agility
Authors: Al-Dwairi Radwan Moh’d , Al-Khataybeh Maali , Najadat Dania , Rawashdeh Adnan
Published: July 2024

The primary objective of this paper is to investigate the interplay between individual self-efficacy (SE) and the adoption of business intelligence (BI) tools, and their combined effects on organizational agility and performance. This research offers a novel perspective by examining the relationship between individual SE and BI tools together, which was neglected in the previous research, shedding light on how these factors collectively influence organizational performance and agility. The importance of this study addresses the crucial need for understanding the role of individual capabilities in leveraging BI tools, especially in the context of rapidly changing environments. The study employs a quantitative approach to examine the proposed model. A survey was conducted with 174 respondents from private and public organizations in Jordan. The findings reveal significant and positive impacts of individual experiences, vicarious experiences (VE), and psychological feedback (PS) on SE. Moreover, the study demonstrates that SE significantly and positively influences the utilization of BI tools, consequently affecting organizational agility and performance. The significance of the study findings lies in its ability to bridge the gap between individual capabilities and the effective utilization of BI tools to equip businesses with invaluable insights for enhancing their decision-making processes.
Modelling a neural network for analysing the results of segmentation of satellite images
Authors: Kaldarova Mira , Akanova Akerke , Naizagarayeva Akgul , Kazanbayeva Albina , Ospanova Nazira
Published: July 2024

The study's relevance lies in addressing inaccuracies within satellite image segmentation, necessitating the development and implementation of neural network models for automated segmentation. The purpose of study is to develop a model of a neural network for training with data obtained from the segmentation of satellite images. The basis of the methodological approach in study is a combination of methods of system analysis of neural networks, which have had a substantial impact on the development of the computer vision industry, with an empirical study of the general principles of neural network modelling for the training on satellite images segmentation. In this study, the results were obtained, indicating that there is a fundamental possibility of developing and practical implementation of a neural network model to determine the quality of the obtained segmentation of images of agricultural fields. Satellite images of agricultural fields of the Republic of Kazakhstan are obtained, and segmentation of field images is performed using the developed neural network model for learning segmentation results. The practical importance of the results obtained in study lies in the possibility of their use in the development of functional models of neural networks for training the results of the segmentation of satellite images.
An optimal machine learning-based algorithm for detecting phishing attacks using URL information
Authors: Devaraj Nandeesha Hallimysore , Thimappa Prasanna Bantiganahalli
Published: July 2024

In recent years, more websites have been collecting personal information for many processes, such as banks, internet connections, and government services. The public needs to provide all personal information, such as Aadhar, PAN, date of birth, and phone number. The personal and sensitive information is at risk of being used for phishing attacks through URL manipulation. In addition, a phishing attack cause’s financial and reputational loss. Hence protecting sensitive information by adapting required protection is extremely valuable for global security. To overcome this, we proposed a method to detect phishing attacks based on previous history, including the duration of operation, customer reviews, web traffic, and the URL. Based on these parameters, the proposed optimal machine learning-based algorithm (OmLA) analyze the previous information about URLs and predict whether it is phishing- or legitimate. As per simulation and performance analysis, the proposed method outperforms conventional methods such as random forest (RF), support vector machine (SVM), and genetic algorithms (GA) by 8%, 18%, and 23%, respectively in terms of accuracy. Additionally, it achieves detection times of 0.2%, 0.6%, and 0.9%, respectively, and excels in response times of 0.45%, 0.56%, and 0.62%, respectively.
Dimensionality reduction for off-line object recognition and detection using supervised learning
Authors: Awwad Sari , Al-Rababa’a Ahmad , Taamneh Salah , El-Salhi Subhieh M. , Mughaid Ala
Published: July 2024

Object recognition and detection is an area of study, within intelligence and computer vision. It finds applications in fields such as surveillance, detailed activity analysis, robotics and object tracking. The primary focus of research papers in this domain revolves around enhancing the precision of object identification and detection regardless of whether the objects are located indoors or outdoors. To address this challenge, a new approach involving the utilization of SIFT features for information extraction has been proposed. Our approach encompasses two components; the implementation of dimensionality reduction through principal component analysis (PCA) to eliminate redundancies; secondly the incorporation of feature vector encoding using fisher encoding techniques. The RGB-D dataset employed contains 300 objects across scenarios with emphasis on colored aspects rather than depth. The SIFT features are categorized using a support vector machine (SVM) into 7 classes. When compared to using SIFT features integrating them with encoding methods notably enhances recall, precision and F1-score by than 30% through fisher encoding and PCA techniques. The study concludes with an evaluation based on n-cross validation methodology along, with detailed experimental results.
Experimental study of a medical data analysis model based on comparative performance of classification algorithms
Authors: Ismukhamedova Aigerim , Uvaliyeva Indira , Rakhmetullina Zhenisgul
Published: July 2024

This article centers around the development and analysis of machine learning (ML) and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of ML and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain.
A novel AI-AVO approach for maximum power generation of PMSG
Authors: S. Chinamalli Prashant Kumar , Sasikala Mungamuri
Published: July 2024

Permanent magnet synchronous generators (PMSGs) are necessary for producing wind energy that is both highly reliable and reasonably priced. An inventive control technique for the driven interior PMSG (IPMSG) is presented here to maximize wind energy output and decrease losses. This research established an innovative optimization strategy for the highest wind power generation with reduced overall loss in PMSG-based Wind power generation systems. Considering, that the tip speed ratio (TPR), rotor speed 𝜔𝑟 , and quadrature axis current 𝐼𝑞 are optimized in the proposed work in such a way to enhance wind power generation. Further, the direct axis current 𝐼𝑑 is calculated from the optimized rotor speed 𝜔𝑟. The minimization of core loss is considered as the fitness function, which is a function of the direct current axis 𝐼𝑑and quadrature current axis 𝐼𝑞. The optimization is carried out using the explored aquila with African vulture optimization (EA-AVO) technique, which is the conceptual incorporation of prevailing techniques, like the aquila optimization algorithm (AOA) and the AVO algorithm. The performance of the proposed method is validated over the conventional methods, in terms of power output, losses, efficiency, and convergence analysis. According, the findings show that the proposed method attains less overall loss of 149.62 at the starting stage of 50 rotor speed, and it was 36.46% higher than AQO, 36.17% higher than AVOA, 36.59% higher than GOA methods 36.42%, and higher than WHO+PI approaches.
O que resta das Luzes? Os direitos do homem à prova dos estudos pós-coloniais
Authors: Spector Céline , Costa Martha Gabrielly Coletto
Published: July 2024

Tradução do artigo original em francês do texto SPECTOR, Céline. “Que reste-ildeslumières? Les droits de l’homme à l’épreuve des études postcoloniales”. Lumières, n. 32, Controverses sur les Lumières 2, pp. 45-60, 2019.
Ideologias políticas e representação democrática: um estudo a partir de Michael Freeden e Nadia Urbinati
Authors: Graeff Patricia
Published: July 2024

The crisis of political representation and the current threats to liberal democracies are issues of large reflection and debate in contemporary political philosophy, what partly explains the renewed interest in the concept of political representation. This article sustains that the understanding of political ideologies, built on Michael Freeden’s morphological approach, enables new forms of thinking about representation and its relation to democracy. By ascribing stable and determined meanings to essentialy contested concepts, ideologies dispute over the control of policial language and also perform an integrative function, simbolically attaching and individual to its society, allowing the sharing of the contentes of the individual conscience. From Nadia Urbinati’s work, it defends the centrality of recovering the ideological dimension in politics so as to attain a notion of democratic representation, which implies to comprehend the deliberative role of political ideologies and the hybrid character of this systems of thought, which contain racional and non-rational elements.
Do príncipe ao conspirador: o Maquiavel de Lefort e a crítica ao idealismo democrático
Authors: Negreiros Dario de
Published: July 2024

Da tripla crítica que Claude Lefort afirmou encontrar em Maquiavel – à tirania, ao conservadorismo burguês e ao idealismo democrático – pouca atenção se costuma dar à última. Neste artigo, procuraremos demonstrar que a obra maior do filósofo francês , Le Travail de l'œuvre, Machiavel (1972), para além de uma crítica à tirania e ao domínio oligárquico, constitui sobretudo uma longa demonstração do modo como a democracia acaba por encontrar seu mais potente inimigo na imagem idealizada de si mesma. Seguindo os passos do Maquiavel de Lefort, veremos emergir ao primeiro plano a personagem do conspirador, que trará à cena consigo a proposição da inevitabilidade da impostura do poder e a defesa da imprescindibilidade e da legitimidade do ato ilegal empreendido contra a legalidade ilegítima.
Paris
Authors: Bastos Mariana Cobuci Schmidt
Published: July 2024

Exercício de estilo para a edição "Escritoras críticas". Trata-se de um breve comentário crítico-poético a respeito dos sentidos e efeitos de "Paris" dentro da poética (considerando em especial os seus desenhos) de Ana Cristina Cesar.
A Tuberculose em São Paulo
Authors: Grieco João , Cardoso Francisco A.
Published: July 2024

Os A. A. estudam a situação epidemiológica do Estado de São Paulo e, mais particularmente, da cidade de São Paulo, em relação à tuberculose. Apresentam as curvas dos coeficientes específicos de mortalidade relativos ao Estado de S. Paulo (1920-1935) e à cidade de S. Paulo (1920-1938). A curva representativa da mortalidade por tuberculose, na cidade de S. Paulo, mostra nítida tendência ascencional. Em 1938, o coeficiente específico de mortalidade na cidade de S. Paulo foi 137,24 por 100. 000 habitantes. Estudam, a seguir, a distribuição geográfica dos óbitos por tuberculose nos municípios do Estado de S. Paulo. Analisam o atual armamento antituberculoso paulista, que acham insuficiente. São discutidas as bases para a luta antituberculosa em S. Paulo. Insistem na formação de um organismo central de luta, destinado a coordenar e a orientar todos os trabalhos oficiais ou de iniciativa privada, consoante um plano previamente traçado. Apontam, como elementos básicos da luta, os Dispensários, os Hospitais-Sanatórios e pessoal técnico especializado (tisiologistas e educadoras sanitárias). São de opinião que os Dispensários antituberculosos devem funcionar nos Centros de Saúde existentes por todo o Estado. Segundo os A. A., a localização dos Hospitais-Sanatórios não deve obedecer às condições de clima, e sim aos seguintes principais critérios: população, índice de mortalidade pela tuberculose, meios de comunicação do lugar e sua importância como centro médico. Acreditam que a preservação da infância depende principalmente das medidas tomadas para o combate da tuberculose do adulto. Em relação ao B. C. G., acham que, em face da insuficiência da atual organização antituberculosa em S. Paulo, a sua aplicação será inútil, senão mesmo nociva. A opinião dos A. A. é que o tratamento ambulatório das crianças doentes deve ser feito nos Dispensários dos Centros de Saúde e a hospitalizaçãodas mesmas, quando necessária, deve ser num pavilhão anexo a um Hospital-Sanatório
Ensáios de calorimetria alimentar II
Authors: Souza G. H. de Paula , Wancolle Alexandre , Barsotti Rolando H.
Published: July 2024

Na apreciação do valor energético dos alimentos, vários processos podem ser utilizados. Sobressai, entretanto, o uso daa bomba calorimétrica, sendo a de Berthelot-Mahler de clássica utilização. Em nosso laboratório já tivemos oportunidade de, por meio dessa aparelhagem, proceder ao exame de uma série de produtos alimentícios, trabalho que publicamos em nosso boletim n. 6, com a colaboração do Prof. Luiz Adolfo Wanderley.
A prova do alcool nos aparelhos de metabolismo
Authors: Ribeiro B. Alves
Published: July 2024

Encarece-se a importância da verificação prévia dos aparelhos destinados a medir o metabolismo energético, acentuando-se o valor da prova do alcool. São referidos os resultados desta prova, num aparelho de circuito fechado, empregando o método de Benedict. Descreve-se, a seguir, uma modificação do dispositivo de Benedict que permite a avaliação gravimétrica do alcool consumido, e dão-se os resultados conseguidos com esta modificação.
Reação da fixação do complemento na tripanosomose americana experimental da cobaia, feita com antígeno de cultura de Trypanosoma cruzi Chagas, 1909 (Tipo Kelser)
Authors: Cardoso F.A.
Published: July 2024

O autor preparou antígeno para a reação de fixação de complemento, segundo a técnica de KELSER, partindo de culturas de 3 amostras de Trypanosoma cruzi Chagas, 1909. Com êsse antígeno o autor praticou 73 reações com o sôro de cobaias inoculadas co.m 4 amostras de T. cruzi, e com o sôro de cobaias normais. Chegou às seguintes conclusões: a) a reação é positiva nas cobaias portadoras de infecção experimental pelo T. cruzi e negativa nas que não o são; b) não há preferência, quanto à intensidade da positividade da reação, para o antígeno homólogo; c) o poder antigênico dos antígenos .varia segundo as diferentes ·amostras que serviram à' sua preparação; daí a necessidade do emprêgo unicamente de amostras de alto poder antigênico no preparo do antígeno destinado à reação da fixação do complemento; d) os anticorpos são de aparecimento precoce e persistentes mesmo após o desaparecimento dos tripanosomas do sangue circulante.
Algumas provas de imunização e neutralização cruzadas entrre o virus neurotrópico de febre amarela clássica e o virus isolado de doentes de febre amarela silvestre na epidemia de 1936-1937, no Estado de São Paulo
Authors: Assumpção Lucas de
Published: July 2024

Existe imunização cruzada entre as amostras ou fontes de virus por nós isoladas de doentes de febre amarela silvestre. Entre essas amostras de virus e o virus neurotrópico da febre amarela clássica, também existe imunização cruzada.As provas de neutralização cruzada também foram positivas entre as amostras isoladas dos doentes de febre amarela silvestre. Há neutralização entre as fontes de virus isoladas desses doentes e o virus neurotrópico da febre amarela clássica. Portanto, há identidade imunológica entre o vírus por nós isolado de doentes de febre amarela silvestre - na epidemia ocorrida no Estado de São Paulo em 1936-1937 e o vírus de febre amarela clássica.
Relações imunitárias entre febre amarela e outras infecçoes clinicamente parecidas que se observam nos climas tropicais e sub-tropicais
Authors: Assumpção Lucas de
Published: July 2024

Se quisermos estudar as relações imunitárias entre febre amarela e outras doenças infecciosas clinicamente parecidas, julgamos indispensável, em primeiro lugar, verificar se há identidade etiológica entre a febre amarela clássica e a febre amarela silvestre. De início deixamos afirmado aqui, baseado em nossos próprios estudos, feitos com o virus isolado de doentes de febre amarela silvestre em confronto com o virus da febre amarela clássica, a nossa convicção de que se trata do mesmo virus, principalmente pelas provas imunitárias a que os submetemos.
Organização da luta anti-tuberculosa na cidade e no campo
Authors: Souza R. de Paula
Published: July 2024

A tuberculose nas capitais do Brasil c zonas rurais de antiga penetração se apresenta em fase epidêmica ascensional. A tuberculose nas zonas do interior do Brasil de mais recente penetração. estão apenas iniciando sua tuberculização. A luta anti-tuberculosa nu Brasil está ainda desorganizada. esparsa e ineficiente. Toda ela gira em torno dos Sanatórios e como estes primam pela ausência, nada se faz profilaticamente. É imprescindível transferir do Sanatório para o Dispensario, o sentido da luta. desenvolvendo-se vasta rede dispensarial. A eficiência dos dispensários, está na razão direta do diagnóstico em massa e tratamento colapsoterápico precoces: a roentgenfotografia de Abreu. precisa ser erigida em l.a plana para o combate à tuberculose. A roentgenfotografia no Brasil, precisa seguir novos rumos, ampliando a preocupação de recenseamento ele grupos representativos, para a da penetração a fundo na população e descobrir precocemente a tuberculose. A equipe movel roentgenfotográfica e a que mais se presta para as condições elo Brasil, não só para os grupos homogêneos urbanos como pela rarefação ele nossa população rural. O programa para leitos no Brasil. não eleve depender unicamente do Estado. precisando ser executado com a colaboração de toda a coletividade. As coletividades organizadas. precisam se orientar para a luta contra a tuberculose. provendo-se ele leitos para seus beneficiários e estendendo o Dispensário a eles e suas famílias. É imprescindível a segregação no tratamento precoce da tuberculose, devendo nessas organizações ser conservados os ordenados integrais para aposentadoria, facilitada e custeada a hospitalização, com reembolso por parte dos curados, suprimido os prazos de carência e ser dada assistência antes mesmo de completado o processo ele aposentadoria. Para. eficiência ela luta anti-tuberculosa é necessário o preparo de tisiólogos. com espírito dínico sanitário.
First record of Scaly-breasted Munia Lonchura punctulata (Linnaeus, 1758) (Aves: Passeriformes: Estrildidae) from Kashmir, India
Authors: Shafayat Shazia , Ahanger Fayaz Ahmad , Ahmad Tariq , Bhat Bilal A. , Zakir Hussain Najar
Published: July 2024

Scaly-breasted Munia or Spotted Munia Lonchura punctulata, is a small finch-like bird with a conical bill from the family Estrildidae, commonly known as the waxbills, grass finches, or munias. The current literature reports Scaly-breasted Munia only in Jammu province of the Union Territory of Jammu & Kashmir, which is an ecologically different landscape from Kashmir. Here we present the first photographic proof of Scaly-breasted Munia at different sites in the Kashmir region of India. During an assessment of north Kashmir’s avian diversity, the Scaly-breasted Munia was sighted at several locations in the area. The bird was identified based on its plumage colour pattern and dark bill.
New distribution record of Slender Wild Basil Clinopodium gracile (Benth.) Kuntze (Lamiaceae: Nepetoideae: Mentheae) for the flora of Himachal Pradesh, India
Authors: Chandra Rimjhim , Kalita Mamita
Published: July 2024

Clinopodium gracile (Benth.) Kuntze has been recorded for the first in the Chamba district of Himachal Pradesh. The study provides descriptions, illustrations, and photographs to aid in the identification of the species. The new distribution record has been entrenched upon investigation based on observation and verification obtained from herbarium, literature, and plant web databases.
Outcomes With the WATCHMAN FLX in Everyday Clinical Practice From the NCDR Left Atrial Appendage Occlusion Registry
Authors: Kapadia Samir R. , Yeh Robert W. , Price Matthew J. , Piccini Jonathan P. , Nair Devi G. , Bansal Agam , Hsu Jonathan C. , Freeman James V. , Christen Thomas , Allocco Dominic J. , Gibson Douglas N.
Published: July 2024

BACKGROUND:

PINNACLE FLX (Protection Against Embolism for Nonvalvular AF Patients: Investigational Device Evaluation of the WATCHMAN FLX LAA Closure Technology) demonstrated improved outcomes and low incidence of adverse events with the WATCHMAN FLX device in a controlled setting. The National Cardiovascular Disease Registry’s Left Atrial Appendage Occlusion Registry was utilized to assess the safety and effectiveness of WATCHMAN FLX in contemporary clinical practice in the United States.

METHODS:

The WATCHMAN FLX Device Surveillance Post Approval Analysis Plan used data from the Left Atrial Appendage Occlusion registry to identify patients undergoing WATCHMAN FLX implantation between August 2020 and September 2022. The key safety end point was defined as all-cause death, ischemic stroke, systemic embolism, or device or procedure-related events requiring open cardiac surgery or major endovascular intervention between device implantation and hospital discharge. Major adverse events were reported at hospital discharge, 45 days, and 1 year.

RESULTS:

Among 97 185 patients in the Left Atrial Appendage Occlusion registry undergoing WATCHMAN FLX, successful implantation occurred in 97.5% (n=94 784) of patients. The key safety end point occurred in 0.45% of patients. At 45 days post-procedure, all-cause death occurred in 0.81% patients, ischemic stroke in 0.23%, major bleeding in 3.1%, pericardial effusion requiring intervention in 0.50%, device-related thrombus in 0.44%, and device embolism in 0.04% patients. No peri-device leak was observed in 83.1% of patients at 45 days. At 1 year, the rate of all-cause death was 8.2%, the rate of any stroke was 1.5% (ischemic stroke, 1.2%), and major bleeding occurred in 6.4% of patients.

CONCLUSIONS:

In a large contemporary cohort of patients with the WATCHMAN FLX device, the rates of implant success and clinical outcomes through 1 year were comparable with the PINNACLE FLX study, demonstrating that favorable outcomes achieved in the pivotal approval study can be replicated in routine clinical practice.
Improving source estimation of retinotopic MEG responses by combining data from multiple subjects
Authors: Hietala Paavo , Kurki Ilmari , Hyvärinen Aapo , Parkkonen Lauri , Henriksson Linda
Published: July 2024

Magnetoencephalography (MEG) is a functional brain imaging modality, which measures the weak magnetic field arising from neuronal activity. The source amplitudes and locations are estimated from the sensor data by solving an ill-posed inverse problem. Commonly used solutions for these problems operate on data from individual subjects. Combining the measurements of multiple subjects has been suggested to increase the spatial resolution of MEG by leveraging the intersubject differences for increased information. In this article, we compare 3 multisubject analysis methods on a retinotopic mapping dataset recorded from 20 subjects. The compared methods are eLORETA with source-space averaging, minimum Wasserstein estimates (MWE), and MWE with source-space averaging. The results were quantified by the geodesic distances between early (60–100 ms) MEG peak activations and fMRI-based retinotopic target points in the primary visual cortex (V1). By increasing the subject count from 1 to 10, the median distances decreased by 6.6–9.4 mm (33–46%) compared with the single-subject median distances of around 20 mm. The observed peak activation locations with multisubject analysis also comply better with the established retinotopic maps of the primary visual cortex. Our results suggest that higher spatial accuracy can be achieved by pooling data from multiple subjects. The strength of MWE lies in individualized and sparse source estimates, but in our data, averaging eLORETA estimates across individuals in source space outperformed MWE in spatial accuracy.
Dynamic and Thermodynamic Environmental Modulation of Tropical Congestus and Cumulonimbus in Maritime Tropical Regions
Authors: Freeman Sean W. , Posselt Derek J. , Reid Jeffrey S. , van den Heever Susan C.
Published: July 2024

We have quantified the impacts of varying thermodynamic environments on tropical congestus and cumulonimbus clouds (CCCs) within maritime tropical regions. To elucidate this relationship, we employed the Regional Atmospheric Modeling System (RAMS) to conduct high-resolution (1 km) simulations of convection over the Philippine Archipelago for a month-long period in 2019. We subsequently performed a cloud-object-based analysis, identifying and tracking hundreds of thousands of individual CCCs using the Tracking and Object-Based Analysis of Clouds (tobac) tracking library. Using this object-oriented dataset of tracked cells, we examined differences in individual storm strength, organization, and morphology due to the storm’s initial environment. We found that storm strength, defined here as maximum midlevel updraft velocity, was controlled primarily by convective available potential energy (CAPE) and precipitable water (PW); high CAPE (>2500 J kg−1) and high (approximately 63 mm) PW were both required for midlevel CCC updraft velocities to reach at least 10 m s−1. Of the CCCs with the most vigorous updrafts, 80.9% were also in the upper tercile of precipitation rates, with the strongest precipitation rates requiring even higher PW. Further, we found that vertical wind shear was the primary differentiator between organized and isolated convective storms. Within the set of organized storms, linearly oriented CCC systems have significantly weaker vertical wind shear than nonlinear CCCs in low- (0–1, 0–3 km) and midlevels (0–5, 2–7 km). Overall, these results provide new insights into the environmental conditions determining the CCC properties in maritime tropical regions.
Cross-Time-Scale Analysis of Year-Round Atmospheric Circulation Patterns and Their Impacts on Rainfall and Temperatures in the Iberian Peninsula
Authors: Olmo Matías , Cos Pep , Muñoz Ángel G. , Altava-Ortiz Vicent , Barrera-Escoda Antoni , Campos Diego , Soret Albert , Doblas-Reyes Francisco
Published: July 2024

This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950–2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations significantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, interannual, and decadal scales, including significant long-term trends on their frequency. CPs influence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures—the subtropical Azores high—leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.