bluuebunny/crossref_metadata_2025 huggingface dataset:
Big data analysis and its impact on the marketing industry: a systematic review
Authors: Patricio-Peralta Cesar , Mondragon Jesús Zamora , Terrones Luis Segura , Villacorta Jimmy Ramirez
Published: June 2024

This systematic review focused on understanding the impact of big data on marketing productivity, following the guidelines of systematic literature reviews and using the PICOC (problem/population, intervention, comparison, results, context) method. 50 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science and business, and published between 2020 and 2023. These articles, selected for their relevance and contribution to the study objectives, showed that the big data offers notable benefits in the marketing industry. The ability to customize marketing strategies to individual customer needs, improved optimization, and a better understanding of customer behaviors and preferences were key aspects. These findings highlight how big data can boost productivity in marketing, strengthening customer relationships and increasing loyalty by improving understanding and adaptation to the specific demands and preferences of each customer. This deeper, more personalized approach to consumers represents a significant shift in the effectiveness and efficiency of marketing strategies in the current era.
Personal identification system based on multidimensional electroencephalographic signals
Authors: Abdel-Gahffar Eman A. , Salama May A
Published: June 2024

Personal authentication using electroencephalographic (EEG) signals, is one of the important applications in brain computer interface (BCI). In this work we investigate the use of EEG signals as a biometric trait. Multidimensional EEG signals were represented as symmetric positive-definite (SPD) matrices on a Riemannian manifold. Two experiments are performed in the first; we use minimum distance to Riemannian mean (MDRM) as a classifier. In the second; SPD matrices are vectorized, and the generated vectors are used to train various machine learning (ML) classifiers. MDRM classifier achieved a correct recognition rate (CRR) of 96.92% , while ML classifiers achieved CRR from 95.39% to 99.45%.
Defence against adversarial attacks on IoT detection systems using deep belief network
Authors: Sharipuddin Sharipuddin , Winanto Eko Arip
Published: June 2024

An Adversarial attack is a technique used to deceive machine learning models to make incorrect predictions by providing slightly modified inputs from the original. Intrusion detection system (IDS) is a crucial tool in computer network security for the detection of adversarial attacks. Deep learning is a trending method in both research and industry, and this study proposes the use of a deep belief network (DBN). DBN can recognize data with small differences, but is also vulnerable to adversarial attacks. Therefore, this research suggests an internet of things-intrusion detection system (IoT-IDS) architecture using a DBN that can counter adversarial attacks. The chosen adversarial attack for this study is the fast gradient sign method (FGSM) used to evaluate the IoT IDS using the DBN model. Testing was conducted in two scenarios: first, the model was trained without adversarial attacks; second, the model was trained with adversarial attacks. The test results indicate that the DBN model struggles to detect FGSM attacks, achieving an accuracy of only 46% when it is not trained with adversarial attacks. However, after training with the FGSM dataset, the DBN model successfully detected adversarial attacks with an accuracy of 97%.
Text document clustering using mayfly optimization algorithm with k-means technique
Authors: Dodda Ratnam , Babu Alladi Suresh
Published: June 2024

Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequency-inverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS).
Machine learning for real estate valuation: Astana, Kazakhstan case
Authors: Barlybayev Alibek , Sankibayev Arman , Niyazova Rozamgul , Akimbekova Gulnara
Published: June 2024

Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.
Hybrid model for sentiment analysis combination of PSO, genetic algorithm and voting classification
Authors: Srivastava Garima , Singh Vaishali , Kumar Sachin
Published: June 2024

As social network services like Weibo and Twitter have grown in popularity, natural language processing (NLP) has seen a great deal of interest in sentiment analysis of social media messages and Information mining. Social media users, whose numbers are always increasing, have the ability to exchange information on their platforms. The study of sentiment, domains and themes are closely related. Manually collecting enough labelled data from the vast array of subjects covered by large-scale social media to train sentiment classifiers across several domains would be extremely difficult. The literature review conducted concludes that models already proposed in the previous researches are not able to achieve good accuracy. This work suggests a unique model that combines of genetic algorithm and particle swarm optimization to effectively extract the features and then the voting technique is applied for the classification. Model proposed is compared with 4 ensemble datasets achieving a consistent accuracy of more than 90% for three different diversified database owing to natural selection of sequences by GA and at the same time achieves a fast convergence with PSO, the model may be employed for highly accurate recommenders demanding precision and accuracy.
Optimizing wireless sensor networks using centrality metrics: a strategic approach
Authors: Kallakunta Suneela , Sreenivas Alluri
Published: June 2024

This research paper presents a methodology for improving wireless sensor network (WSN) performance by leveraging centrality measures, including degree, betweenness, closeness, eigenvector, and Katz centrality. Employing a random walk graph model, this study constructs networks with 30 and 50 nodes to investigate the impact of these centrality metrics on routing decisions to optimize energy efficiency, minimize latency, and enhance overall network reliability. Additionally, the paper provides a comprehensive analysis of the relationships among these centrality measures through various correlation techniques, such as Pearson correlation, Kendall rank correlation, and Spearman correlation, offering insights into how these metrics can effectively improve WSN operations.
Recognizing gender from images with facial makeup
Authors: Micheal Annie , Palanisamy Geetha
Published: June 2024

Recognizing the sex of an individual is a difficult task due to pose variation, occlusion, illumination effect, facial expression, plastic surgery, and makeup. In this manuscript, a novel approach for gender recognition with facial makeup is proposed. A novel Log-Gabor COSFIRE (LG-COSFIRE) filter is a shape-selective filter that is trained with prototype patterns of interest. The geometrical structure of the faces is acquired using the dual-tree complex wavelet transform (DT-CWT). Dense SIFT descriptor extracts the shape attributes of an image by building local histograms of gradient orientation. Finally, least square support vector machine (LS-SVM) is utilized to recognize the gender of an individual. The experiment was performed on self-built facial makeup for male and female (FMMF) database and achieves 89.7% accuracy.
Android malware detection using GIST based machine learning and deep learning techniques
Authors: Udayakumar Ponnuswamy , Yalamati Srilatha , Mohan Lavadiya , Haque Mohd Junedul , Narkhede Gaurav , Bhashyam Krishna Mohan
Published: June 2024

In today’s digital world, Android phones play a vital part in a variety of facets of both professionals and individuals’ personal and professional lives. Android phones are great for getting things done faster and more organized. The proportionate increase in the number of malicious applications has also been seen to be expanding. Since the play store offers millions of apps, detection of malware apps is challenging task. In this paper, a methodology is introduced for detecting malware in Android applications through the utilization of global image shape transform (GIST) features extracted from grayscale images of the applications. The dataset comprises samples of both malware and benign apps collected from the virus share website. After converting the apps into grayscale images, GIST features are extracted to capture their global spatial layout. Various machine learning (ML) algorithms, such as logistic regression (LR), k-nearest neighbour (KNN), AdaBoost, decision tree (DT), Naïve Bayes (NB), random forest (RF), support vector machine (SVM), extra tree classifier (ETC), and gradient boosting (GB), are employed to classify the applications according to their GIST features. Furthermore, a feed forward neural network (FFNN) is utilized as a deep learning (DL) technique to further improve the accuracy of classification. The performance of each algorithm is evaluated using metrics such as accuracy, precision and recall. The results demonstrated that the FFNN achieves superior accuracy compared to traditional ML classifiers, indicating its effectiveness in detecting malware in Android apps.
Hybrid fuzzy logic and gravitational search algorithm based routing for wireless sensor networks
Authors: Ramappa Shwetha Golluchinnappanahalli , Subbarao Venkata Narayana Murthy
Published: June 2024

Over the recent years, the wireless sensor network (WSN) has garnered significant attention from both researchers and the general populace. Its application in diverse environmental scenarios, including weather monitoring, temperature regulation, humidity tracking, and military surveillance, extends beyond conventional boundaries. WSNs consist of numerous nodes, each functioning as a sensor with the primary responsibility of data sensing. These nodes operate under constraints such as power, energy, efficiency, and deployment considerations. Moreover, the power and other resources cannot be replaced and renewed therefore prolonging the network lifetime has become the main aspect of WSNs. Energy aware routing play’s important role to ensure the efficient data transmission with minimal power consumption and ensures the prolonged network lifetime. In this work, we focus on optimizing the routing process therefore we present a hybrid model which uses fuzzy logic for path identification and gravitational search optimization (GSA) for efficient path selection. The fuzzy logic considers energy consumption, residual energy, distance and delay parameters to identify the most suitable path for data transmission. The experimental analysis shows a significant improvement in network lifetime, delay an aspect delivery.
Advanced control of double stage grid-tied three phase photovoltaic systems with shunt active power filter
Authors: Zaghar Fatim-Zahra , Hekss Zineb , Rafi Mohamed , Ridah Abderraouf , Adhiri R’hma
Published: June 2024

This paper explores the challenges associated with the control of a two-stage three-phase electrical grid connected to a photovoltaic (PV) system. The objectives encompass: i) maximizing the available PV power, ii) controlling the DC-link voltage to a predetermined setpoint, and iii) considering that power quality has become an important measure in a distribution electrical network where different loads are connected, the third objective will mainly focus on ensuring power factor correction (PFC). To achieve these objectives, two loops of nonlinear controller are developed. In the outer loop, the duty cycle of a boost converter is controlled using a hybrid technique of backstepping technique and the perturb & observe (P&O) algorithm. In addition, the inner loop employs a hybrid automaton approach to tackle the challenges of a three-phase shunt active power filter (SAPF). The results have been verified through numerical simulation using MATLAB/Simulink power systems environment.
Investigations of BLDC motor speed characteristics via THD under conventional and advanced hybrid controllers
Authors: Chaitanya Kumar Reddy Kamatam Muni Naga , Kanagasabai Nallathambi
Published: June 2024

This project investigates brushless direct current (BLDC) motor speed control through total harmonic distortion (THD) analysis, employing proportional integral (PI), fuzzy logic (FLC), adaptive neuro-fuzzy inference system (ANFIS), and an innovative hybrid ANFIS-PD/PI controller. Considering the vital role of BLDC motors in precision-dependent industries like robotics, electric vehicles, and industrial automation, our primary focus is on understanding BLDC motor operation and recognizing THD's significance as a performance metric. Controllers are meticulously implemented in real-time, fine-tuned, and optimized to achieve desired speed characteristics, incorporating considerations like response time, accuracy, and energy efficiency. The project's core involves THD analysis, quantifying harmonic content in the BLDC motor's speed waveform. This facilitates a comprehensive comparative evaluation of controller performance, assessing their capability to maintain speed stability and influence power quality. The discussion covers the merits and limitations of each controller, with a special emphasis on the hybrid ANFIS-PD/PI controller, seamlessly blending ANFIS adaptability with PD/PI control stability. Results illustrate the hybrid controller's excellence in optimizing BLDC motor speed control, demonstrating superior performance in speed accuracy, disturbance rejection, and THD reduction. These findings drive advancements in motor control technology, providing practical guidance for selecting controllers tailored to specific application requirements. Simulation results can be analyzed using MATLAB/Simulink 2018a Software.
IoT-based system to detect and control natural gas leaks in residential kitchens
Authors: Pillco-Sanchez Max Jhonatan , Huatuco-Villanueva Alex Antonio , Sanchez-Ramirez Jhony Miguel , Cabana-Cáceres Maritza , Castro-Vargas Cristian
Published: June 2024

Natural gas is widely used in many homes for cooking, but a lack of gas leak detection has led to large fires and accidents. This article presents the design and implementation of a natural gas detection and extraction system for domestic kitchens in Lima, Peru. The ESP32 microcontroller allowed remote circuit control, resulting in a more convenient setup than the Arduino UNO microcontroller. After calibration of the sensors and their corresponding programming, three actions were established in response to different gas levels: alarm activation, space ventilation and gas extraction, and thermal shutdown. Strategic sensor placement and improved physical presentation of the system were performed to ensure accurate readings and effective deployment. The results demonstrate the proper functioning of the circuit and its ability to prevent accidents related to gas leaks. The designed system offers the advantage of remote monitoring, providing access to the user from any location. In conclusion, this project offers a comprehensive solution to prevent accidents caused by gas leaks in home kitchens. With satisfactory results in terms of operation and rapid response, the project demonstrates its effectiveness in accident prevention. This design offers a practical and accessible solution, improving security and bringing peace of mind to homes.
Edge-platforms based decision-support approach for solar panels inspection with YOLOv8 deep neural-network
Authors: El Karch Hajar , Mezouari Abdelkader , Natij Youssef , El Gouri Rachid
Published: June 2024

This paper presents an innovative AI-based method for autonomous inspection, designed to enhance energy production efficiency by optimizing cleaning strategies for soiled photovoltaic panels, using advanced artificial intelligence algorithms to analyze panel conditions and environmental factors in real-time, allowing for targeted cleaning interventions. Based on the advanced YOLOv8 deep learning algorithm and computer vision approach, the proposed method offers distinct advantages in real-time detection and classification of various types of soiling and dust accumulation compared on solar panels to traditional methods, and underwent satisfactory testing across diverse scenarios. The NVIDIA Jetson Nano, the Raspberry Pi4 embedded devices, and the Raspberry Pi4 combined with NCS2 accelerator are used for implementing our approach. A comparison aims to provide a detailed exploration of the most suitable embedded platform for deploying our advanced system was discussed. This comparison considers processing speed and accuracy, energy consumption, and overall performance in executing the computationally intensive tasks. The results demonstrate that our model achieves high accuracy in detecting soiling and enhancing the model's detection speed. With an average precision of 99.5%, this approach ensures accurate fault identification, underscoring the effectiveness of computer vision using deep learning algorithms for detection tasks across a wide range of scenarios.
Improving Kui digit recognition through machine learning and data augmentation techniques
Authors: Nayak Subrat Kumar , Nayak Ajit Kumar , Mishra Smitaprava , Mohanty Prithviraj , Tripathy Nrusingha , Prusty Sashikanta
Published: June 2024

Speech digit recognition research is growing decisively, and a bulk of digit recognition algorithms are used in European and a few Asian languages. Kui is a low-resourced tribal language locally used in several states of India. Despite its significance, there is not much research on Kui's speech. This research aims to present an in-depth analysis of novel Kui digit recognition using predefined machine learning (ML) techniques. For this purpose, we first gathered spoken numbers i.e. from 0 to 9 of eight different speakers containing a total of 200 words. Secondly, we choose the numbers: ଶୂନ (zero), ଏକ (one), ଦୁଇ (two), ତିନି(three), ସାରି(four), ପାସ (five), ସଅ (six), ସାତ (seven), ଆଟ (eight), ନଅ (nine). Meanwhile, we build nine different ML models to recognize Kui digits that take the Mel-frequency cepstral coefficients (MFCCs) method to extract the relevant features for model predictions. Finally, we compared the performance of ML models for both augmented and non-augmented Kui data. The result shows that the SVM+Augmentation method for Kui digit recognition combined obtained the highest accuracy of 83% than other methods. Moreover, the difficulties and potential prospects for Kui digit recognition are also highlighted in this work.
Dual image watermarking based on NSST-LWT-DCT for color image
Authors: Avivah Siti Nur , Ernawan Ferda , Mat Raffei Anis Farihan
Published: June 2024

Advanced internet technology allows unauthorized individuals to modify and distribute digital images. Image watermarking is a popular solution for copyright protection and ensuring digital security. This research presents an embedding scheme with a set of conditions using non-subsampled Shearlet transform (NSST), lifting wavelet transform (LWT), and discrete cosine transform (DCT). Red and green channels are employed for the embedding process. The red channel is converted by NSST-LWT. The low-frequency area (LL) frequency is then split into small blocks of 8×8, each partition block is then transformed by DCT. The DCT coefficient of (3,4), (5,2), (5,3), (3,5), called matrix M1, and (2,5), (4,3), (6,2), (4,4), called matrix M2 are selected for singular value decomposition (SVD) process. With a set of conditions, the watermark bits are incorporated into those singular values. The green channel is cropped to get the center image before splitting into 4×4 pixels. The block components are then selected based on the least entropy value for the embedding regions. The selected blocks are then computed using LWT-SVD. A set of conditions for U(1,1) and U(2,1) are used to incorporate the watermark logo. The experimental findings reveal that the suggested scheme achieves high imperceptibility and resilience under various evaluating attacks with an average peak signal-to-noise ratio (PSNR) and correlation value (NC) values are up to 43.89 dB and 0.96, respectively.
Experiencia significativa, “encuentro al compás del folclor y las tradiciones afrocolombianas”
Authors: Molina Marjorie
Published: June 2024

El “Encuentro al Compás del Folclor y las Tradiciones Afrocolombianas”, Experiencia Significativa, liderada por la Institución Educativa Alberto Mendoza Mayor del Municipio de Yumbo (Valle), es un motor que impulsa cambios y promueve transformaciones sociales en medio de contextos pedagógicos de formación humana a través de la lúdica, encaminados a fortalecer la identidad y a propiciar la visibilización de la cultura afrodescendiente, reconociendo su aporte social, político, científico, económico, ambiental y cultural en la construcción de la historia nacional.

El eje central del proyecto es el 21 de Mayo, conmemorado como el día de la Afrocolombianidad, construyendo así patrones de respeto en la diferencia bajo el lema “Identidad e Igualdad”.

La Experiencia está enriquecida por diversos sub-proyectos como: Foro Etnoeducativo Estudiantil, Foro Pedagógico Municipal de Maestros, desfile “Alegría, color e igualdad étnica”, Misa folclórica Inculturada, muestra gastronómica, afroyinkana y olimpiadas del saber “Soy Afro”, además de otras actividades, desarrollándose de esta forma el diálogo permanente y la retroalimentación entre los actores de la comunidad educativa y otros miembros de la sociedad fortaleciéndose así la responsabilidad que tiene el sector educativo con la comunidad en general.

La estrategia pedagógica utilizada y sustentada en la investigación, permite traspasar los muros de la escuela impactando otros sectores a partir de objetivos claros basados en modelos participativos y sensibles promoviendo la interculturalidad y la movilización de saberes orientados al desarrollo humano.
Liceo VAL (vida, amor, luz). “Hacia un aprendizaje significativo y autónomo”
Authors: Esperanza Morales Luz
Published: June 2024

Este artículo hace un acercamiento al autoaprendizaje como método para acceder al conocimiento de forma significativa, autónoma e incluyente. A lo largo del texto se conoce mi historia como docente y se evidencia el camino que ha recorrido el Liceo VAL desde que era mi sueño, hasta ahora que se ha convertido en una alternativa para la formación de niños, niñas y jóvenes, que encuentran en esta institución una experiencia inclusiva real, la cual permite a cada estudiante alcanzar los logros académicos según su ritmo y capacidad. Nuestro modelo posibilita una escuela eficaz para los que tienen NEE, para los denominados estudiantes típicos, y en general, para toda la comunidad. Destaco como un factor de alta incidencia, la formación en valores y convivencia tanto como la capacidad de aceptación y respeto por la diferencia que promueve el liceo y que encarnan todos sus miembros. Finalmente, a través de la pregunta ¿cómo sensibilizar y motivar una experiencia de inclusión para la vida a través del autoaprendizaje? Explico mi práctica, sus beneficios, los retos que plantea y las satisfacciones que ofrece, esperando que se conozca y se multiplique en beneficio de la educación de los niños, niñas y jóvenes colombianos.
Las Ciencias Naturales como dispositivo de nuevos ambientes que inciden en el desarrollo social y ambiental
Authors: Mojica Ruiz Alexander
Published: June 2024

En el siguiente artículo se presenta la fundamentación de la organización escolar del Centro de Aprendizaje ER, diseñada e inferida del modelo didáctico enseñanza por indagación y la pedagogía social. Así mismo, cómo la práctica pedagógica del profesor Alexander Mojica Ruiz, Licenciado en Ciencias Naturales y Educación Ambiental, se ha constituido en una praxis pedagógica de avanzada, desarrollada desde el año 2006 en el colegio Puerto Pinzón (Puerto Boyacá), luego en la Escuela Normal Superior Nuestra Señora del Rosario (Güicán de la Sierra) y actualmente en la sede San José de la institución educativa técnica Ignacio Gil Sanabria del municipio de Siachoque. El Centro de Aprendizaje ER comprende cinco ambientes inmersos en los contextos escolar y comunitario, los cuales potencializan la actitud científica de los/las estudiantes a partir de dos tópicos generativos; la medicina y la situación ambiental del entorno de la escuela. El primero, para conocer el cuerpo humano y comprender las ciencias naturales en el aula. El segundo, para desarrollar investigaciones que fomentan la responsabilidad ética, social y ambiental de la comunidad educativa. Por los resultados en su impacto social y ambiental en la comunidad de Güicán de la Sierra se hace énfasis al segundo tópico.
Tejiendo relaciones… Construimos identidad
Authors: Londoño González Vilma Lucía , Monsalve Arroyave Diana María , Muñoz Castillo Tatiana Alexandra
Published: June 2024

La institución educativa como espacio en el que confluye la diversidad da lugar a encuentros y desencuentros que pueden llegar a ser fuentes de conflictos, es a partir de la observación y análisis de las interacciones manifiestas en la comunidad educativa Débora Arango Pérez que surge el deseo de transformarlas hacia acciones positivas de convivencia, llevando a la reflexión, confrontación y transformación de prácticas sociales.
Siendo la persuasión el elemento esencial para lograr el cambio de actitudes desde el ámbito de la conciencia, se evidencia de manera progresiva mayor pertenencia hacia la institución, con todas las responsabilidades que ello implica. Esta introyección se ve reflejada en el mejoramiento de las relaciones desde y para la comunidad.Las interacciones positivas nos llevan a construir y/o transformar una identidad deboriana que trascienda en el entorno y en su proyecto de vida, a partir de unos valores y unos principios institucionales.
Humanidades digitais nas pesquisas da Alaic, Compós e Intercom (2019-2022)
Authors: Faria Fernanda Sevarolli Creston , Kistemann Junior Marco Aurélio
Published: June 2024

Este trabajo busca comprender cómo las Humanidades Digitales (DH) se han ido aproximando al campo de la Comunicación. La justificación de esta investigación es que el campo de la Comunicación y las Humanidades Digitales han crecido exponencialmente, especialmente en lo que se refiere a sus raíces epistemológicas y a la concreción del campo como importantes áreas de conocimiento. Así, el objetivo general de este trabajo es presentar el estado del arte de las HD dentro del campo de la Comunicación. Para ello, analizamos analógicamente, es decir, manualmente, tres plataformas de datos académicos de eventos del campo de conocimiento de la Comunicación, a saber, Compós, Revista Intercom y Alaic, desde el 1 de enero de 2019 hasta el 31 de diciembre de 2022. Una vez que tuvimos los datos, analizamos los hallazgos, destacamos dos herramientas digitales y concluimos el trabajo reconociendo que la EH puede contribuir a la investigación en Comunicación, pero que aún necesita ser entendida y aplicada en las metodologías de investigación que adopta.
Qual foi o destino do dinheiro do Fundo de Emancipação no pós-abolição? Projetos, protestos e disputas (1884-1890)
Authors: Cruz Itan
Published: June 2024

O objetivo deste artigo é contribuir para o debate público recente sobre a participação do Estado brasileiro e das instituições financeiras na escravidão de africanos e seus descendentes, apontando o destino das verbas que constituíram o fundo nacional de emancipação no imediato pós-abolição. Destacam-se projetos, protestos e disputas em relação ao dinheiro que, a princípio, tinha a finalidade de auxiliar os escravizados na conquista da sua liberdade e que, após o 13 de maio, colocou-se em contestação. Demostram-se as transformações pelas quais esse fundo passou desde a sua criação, em 1871, a implementação de uma taxa para sua ampliação, além de seguir o dinheiro, identificando beneficiários, onerados e ignorados. Foram consultados os anais da Câmara dos Deputados e do Senado do Império, assim como legislações, relatórios ministeriais, periódicos, charges, discursos e livros de época.
Sim, o gaúcho era gay
Authors: Weimer Rodrigo de Azevedo
Published: June 2024

Book review: KOCH, Jandiro Adriano. O gaúcho era gay? Mas bah! (1737-1939). Porto Alegre: MAR Edições, 2023.
Modificações na Família Contemporânea: revisão do que é dito sobre família entre 2010 e 2019
Authors: Silva Caio Monteiro , Neves Beatriz Sernache de Castro
Published: June 2024

Este artigo propõe-se a investigar os processos de mudança que implicam naquilo que se chama famílias contemporâneas. Muito se debate a respeito das novas configurações familiares e suas respectivas características e consequências, sendo este tipo de associação humana ainda privilegiadamente o principal meio de nossos processos de socialização coetâneos torna-se importante e necessário pensar sobre a forma como vem se apresentando a família. Desta forma, através de revisão integrativa e sistemática da literatura dos últimos 10 anos propõe-se reunir e apresentar a partir de unidades semânticas (produção de categorias que reúnem aspectos comuns, sendo essas: mudanças nas relações de gênero; mudança na configuração familiar; amparo jurídico; mudanças nas relações de parentesco; mudança nas relações etárias; expectativas sobre a família; valores familiares) aquilo que a literatura especializada no campo da família aponta como as mudanças fundamentais que identificam os modos como se apresentam contemporaneamente as famílias.
Forecasting the Burden of Cardiovascular Disease and Stroke in the United States Through 2050—Prevalence of Risk Factors and Disease: A Presidential Advisory From the American Heart Association
Authors: Joynt Maddox Karen E. , Elkind Mitchell S.V. , Aparicio Hugo J. , Commodore-Mensah Yvonne , de Ferranti Sarah D. , Dowd William N. , Hernandez Adrian F. , Khavjou Olga , Michos Erin D. , Palaniappan Latha , Penko Joanne , Poudel Remy , Roger Véronique L. , Kazi Dhruv S. ,   on behalf of the American Heart Association
Published: June 2024

BACKGROUND:

Cardiovascular disease and stroke are common and costly, and their prevalence is rising. Forecasts on the prevalence of risk factors and clinical events are crucial.

METHODS:

Using the 2015 to March 2020 National Health and Nutrition Examination Survey and 2015 to 2019 Medical Expenditure Panel Survey, we estimated trends in prevalence for cardiovascular risk factors based on adverse levels of Life’s Essential 8 and clinical cardiovascular disease and stroke. We projected through 2050, overall and by age and race and ethnicity, accounting for changes in disease prevalence and demographics.

RESULTS:

We estimate that among adults, prevalence of hypertension will increase from 51.2% in 2020 to 61.0% in 2050. Diabetes (16.3% to 26.8%) and obesity (43.1% to 60.6%) will increase, whereas hypercholesterolemia will decline (45.8% to 24.0%). The prevalences of poor diet, inadequate physical activity, and smoking are estimated to improve over time, whereas inadequate sleep will worsen. Prevalences of coronary disease (7.8% to 9.2%), heart failure (2.7% to 3.8%), stroke (3.9% to 6.4%), atrial fibrillation (1.7% to 2.4%), and total cardiovascular disease (11.3% to 15.0%) will rise. Clinical CVD will affect 45 million adults, and CVD including hypertension will affect more than 184 million adults by 2050 (>61%). Similar trends are projected in children. Most adverse trends are projected to be worse among people identifying as American Indian/Alaska Native or multiracial, Black, or Hispanic.

CONCLUSIONS:

The prevalence of many cardiovascular risk factors and most established diseases will increase over the next 30 years. Clinical and public health interventions are needed to effectively manage, stem, and even reverse these adverse trends.
Cerebrovascular Effects of Sildenafil in Small Vessel Disease: The OxHARP Trial
Authors: Webb Alastair J.S. , Birks Jacqueline S. , Feakins Karolina A. , Lawson Amy , Dawson Jesse , Rothman Alexander M.K. , Werring David J. , Llwyd Osian , Stewart Catriona R. , Thomas James
Published: June 2024

BACKGROUND:

Vascular cognitive impairment due to cerebral small vessel disease is associated with cerebral pulsatility, white matter hypoperfusion, and reduced cerebrovascular reactivity (CVR), and is potentially improved by endothelium-targeted drugs such as cilostazol. Whether sildenafil, a phosphodiesterase-5 inhibitor, improves cerebrovascular dysfunction is unknown.

METHODS:

OxHARP trial (Oxford Haemodynamic Adaptation to Reduce Pulsatility) was a double-blind, randomized, placebo-controlled, 3-way crossover trial after nonembolic cerebrovascular events with mild-moderate white matter hyperintensities (WMH), the most prevalent manifestation of cerebral small vessel disease. The primary outcome assessed the superiority of 3 weeks of sildenafil 50 mg thrice daily versus placebo (mixed-effect linear models) on middle cerebral artery pulsatility, derived from peak systolic and end-diastolic velocities (transcranial ultrasound), with noninferiority to cilostazol 100 mg twice daily. Secondary end points included the following: cerebrovascular reactivity during inhalation of air, 4% and 6% CO
2
on transcranial ultrasound (transcranial ultrasound-CVR); blood oxygen-level dependent–magnetic resonance imaging within WMH (CVR-WMH) and normal-appearing white matter (CVR-normal-appearing white matter); cerebral perfusion by arterial spin labeling (magnetic resonance imaging pseudocontinuous arterial spin labeling); and resistance by cerebrovascular conductance. Adverse effects were compared by Cochran Q.

RESULTS:

In 65/75 (87%) patients (median, 70 years;79% male) with valid primary outcome data, cerebral pulsatility was unchanged on sildenafil versus placebo (0.02, −0.01 to 0.05;
P
=0.18), or versus cilostazol (−0.01, −0.04 to 0.02;
P
=0.36), despite increased blood flow (∆ peak systolic velocity, 6.3 cm/s, 3.5–9.07;
P
<0.001; ∆ end-diastolic velocity, 1.98, 0.66–3.29;
P
=0.004). Secondary outcomes improved on sildenafil versus placebo for CVR-transcranial ultrasound (0.83 cm/s per mm Hg, 0.23–1.42;
P
=0.007), CVR-WMH (0.07, 0–0.14;
P
=0.043), CVR-normal-appearing white matter (0.06, 0.00–0.12;
P
=0.048), perfusion (WMH: 1.82 mL/100 g per minute, 0.5–3.15;
P
=0.008; and normal-appearing white matter, 2.12, 0.66–3.6;
P
=0.006) and cerebrovascular resistance (sildenafil-placebo: 0.08, 0.05–0.10;
P
=4.9×10
−8
; cilostazol-placebo, 0.06, 0.03–0.09;
P
=5.1×10
−5
). Both drugs increased headaches (
P
=1.1×10
−4
), while cilostazol increased moderate-severe diarrhea (
P
=0.013).

CONCLUSIONS:

Sildenafil did not reduce pulsatility but increased cerebrovascular reactivity and perfusion. Sildenafil merits further study to determine whether it prevents the clinical sequelae of small vessel disease.

REGISTRATION:

URL:
https://www.clinicaltrials.gov/study/NCT03855332
; Unique identifier: NCT03855332.
Food Security Status of Female-Headed Households in Ethiopia
Authors: Mohammed Awol Ali
Published: June 2024

This article aims to explore the food security status of female-headed households and its determinant factors in Sidama Regional State, Ethiopia. To that end, the article used both quantitative and qualitative research approaches, as well as multistage sampling techniques and blending both descriptive and econometric statistics during data analysis. The findings depicted that 40.97% of female-head households were food secure, 15.97% of female headed households were mildly food insecure, 18.75% were highly food insecure, and 24.31% were severely food insecure. The study also involves demographic, socioeconomic, and institutional factors that had a substantial impact on food security in female-headed households in the study area. Finally, the study advised that rural female-headed families receive special care and support in order to improve their livelihoods and food security status, and that livestock holding and cultivated land size play an important role in boosting food security.
Review of Pandara DeLong & Freytag (Insecta: Hemiptera: Cicadellidae: Gyponini), raised to generic status, including three new species and an identification key
Authors: SILVA MARCELO PEIXOTO GOMES DA , GONÇALVES CLAYTON CORRÊA , TAKIYA DANIELA MAEDA
Published: June 2024

The monotypic subgenus Pandara DeLong & Freytag, 1972 of Gyponana Ball, 1920 is reviewed, and proposed to be raised to generic rank, with new diagnostic characters provided. The type-species Gyponana (P.) eleganta DeLong & Freytag, 1972 described from Santa Cruz, Bolivia is redescribed. In addition, three new species for the genus are described and illustrated: Pandara inca sp. nov. from Peru, Pandara indivisa sp. nov. from Peru and Bolivia, and Pandara parallela sp. nov. from Brazil and French Guiana. The female terminalia of a representative of this genus is described for the first time. A species identification key for males and a distribution map for the four known species are provided.
Introduction
Authors: Gaston Bruce
Published: June 2024

This book collects thirteen short stories by ‘Saki’ (Hector Hugh Munro): ten in their original periodical versions and three that had been forgotten until recently. The first ten were rewritten when they were reprinted as part of the volume The Chronicles of Clovis (1911). Reconstructing the composition/publishing process from Munro’s correspondence with his new publisher (John Lane at The Bodley Head), I argue that the title was imposed on Munro, who acquiesced by writing more stories featuring his Clovis character and by reworking older works so that Clovis appeared in them. The intention in doing so was to give the collection more of a thematic unity, and thereby anticipate the difficulty that publishers felt they had in interesting the reading public in volumes of unconnected stories. Munro also made another concession to his publisher in a significantly rewritten and expanded version of his political fantasy ‘Ministers of Grace’, which unfortunately only obscured the satirical thrust of the original version. Similarly, readers will likely prefer the originals of the other stories before they were distorted to include Clovis.

‘Mrs. Pendercoet’s Lost Identity’ is adduced here, being an example of a contemporaneous story that could be given a differently named protagonist because it was not destined to be part of The Chronicles of Clovis. ‘The Romance of Business’, conversely, is a later example of a Clovis story, unusual because it was written on commission. These two rediscovered stories are accompanied by another, ‘The Optimist’, which showcases Munro’s ability to cater for a wider public by writing in a more naturalistic and serious way.
Mrs. Packletide’s Tiger
Authors: Gaston Bruce
Published: June 2024

Mrs. Packletide decides that only a spectacular accomplishment such as shooting a tiger in India will allow her to draw the attention away from her social rival, who has been boasting about a recent aeroplane trip. She offers a reward to anyone who can facilitate this plan, and one Indian village tells her of an old and decrepit tiger in its environs. A trap is set up, so that Mrs Packletide can shoot the tiger without much difficulty or danger. In the end, however, she misses the tiger, but the noise of the gun gives it a heart attack. Mrs Packletide nevertheless makes out that she has shot the tiger and displays its skin in her London house. Her paid companion blackmails her by threatening to reveal the farcical circumstances of the tiger’s death, and with the money buys herself a small house in the country.
The Background
Authors: Gaston Bruce
Published: June 2024

Henri Deplis, a commercial traveller from Luxembourg, is working in Italy when he unexpectedly inherits some money from a distant relative. Prompted by his new wealth, he has his back tattooed by a renowned tattoo artist. Unfortunately, he has already squandered most of his inheritance and cannot pay the bill. The artist dies and his widow presents the artwork to the municipality of Bergamo. In consequence, Deplis suffers a number of interferences from the Italian authorities, who consider the tattoo an important artwork. Deplis finds himself prevented from leaving Italy, as he neither owns the picture nor possesses an export license for it. These experiences lead him into radical politics and the tattoo is damaged in a fight with another anarchist, after which he is expelled from the country as a politically undesirable person. He ends his days in Paris suffering under the delusion that he is one of the lost arms of the Venus de Milo.
The Jesting of Arlington Stringham
Authors: Gaston Bruce
Published: June 2024

Arlington Stringham, an M.P., inadvertently makes a joke during a House of Commons debate. When this is reported in the newspapers, he starts to believe that he is a wit and to make what he thinks are clever comments at every possible opportunity. His wife of many years is disturbed by this sudden change in his character. When she discovers that most of his witticisms have been borrowed from Lady Isobel, a sharp-tongued acquaintance of theirs, she takes an overdose of her sleeping draught.
Filboid Studge
Authors: Gaston Bruce
Published: June 2024

Mark Spayley, a commercial artist, wants to marry the daughter of Duncan Dullamy, a devious businessman. Dullamy agrees if Mark can help sell the new breakfast cereal he has launched. Mark changes its name to the unappetising-sounding “Filboid Studge” and creates an advertising poster showing tortured figures in Hell, with the caption “they cannot buy it now”. This depiction of the cereal as something unpleasant but good for you strikes a chord with many people and it becomes a runaway success. Dullamy makes a fortune and marries his daughter off to somebody of a much higher social standing than the impoverished artist Mark.
Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output
Authors: Lorenz David J. , Otkin Jason A. , Zaitchik Benjamin F. , Hain Christopher , Holmes Thomas R. H. , Anderson Martha C.
Published: June 2024

The effect of machine learning and other enhancements on statistical–dynamical forecasts of soil moisture (0–10 and 0–100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15–28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored.

Significance Statement

Rapidly intensifying droughts pose extra challenges for predictability. Here, dynamical forecast model output is combined with nonlinear machine learning methods to improve forecasts of rapid changes in soil moisture and the evaporative stress index (ESI).