bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient implementation of the functional links artificial neural networks with cross-terms for nonlinear active noise control
Authors: Cong Le Dinh , Anh Mai The
Published: June 2024

This paper proposes an efficient extension of functional links artificial neural networks (EE-FLANN) for the active noise control (ANC) application. The developed EE-FLANN controller can upgrade the model accuracy with the actual system thanks to adding the cross-terms to the trigonometric function. Unlike the method in the generalized FLANN (GFLANN) controller, the EE-FLANN exploits include cross-term symmetry. However, this causes the computational burden to increase remarkably. To reduce this disadvantage, we truncate the cross-terms appropriately based on the simplified strategy. Furthermore, the adaptive algorithm is designed to partially update the filter coefficients appropriately. Specifically, the cross-terms that do not satisfy certain magnitude conditions will be omitted during the update process to reduce costs. Experiments have shown that the proposed EE-FLANN controller can achieve comparable performance to the GFLANN controller but the complexity is reduced by up to 20%.
A fully automatic curve localization method for extracted spine
Authors: Xie Aishu , Moung Ervin Gubin , Zhou Xu , Yang Zhibang
Published: June 2024

The automation of scoliosis positioning presents a challenging and often understated task, yet it holds fundamental significance for the automated analysis of spinal morphological anomalies. This paper introduces a novel spinal curve localization model for precisely differentiating the spinal curves and identifying their concave centers. The proposed model contains three components: i) custom spine central line model, to define the spine central line as a combination of several secant line sequences with different polarities; ii)  custom curve model, to classify each spinal curve into one of 11 curves types and deduce each its concave centers by several custom formulas; and iii) adapted distance transform and quadratic line fitting algorithm coupled with custom secant line segment searching strategy (DTQL-LS), to search all line segments in the spine and group consecutive line segments with identical polarity into line sequence. Experimental results show that its positioning success rate is close to 99%. Furthermore, it exhibits significant time efficiency, with the average time to process a single image being less than 30 milliseconds. Moreover, even if some image boundaries are blurred, the center of the curve can still be accurately located.
Design of an optimized energy-efficient routing protocol for reliable wireless body area networks
Authors: Almutairi Hissah , Alqahtani Abdullah , S. Jabbar Zinah , Fadhil Tawfeq Jamal , Dheyaa Radhi Ahmed , Soon JosephNg Poh
Published: June 2024

Energy limitation is one of the essential parameters in the design of a Wireless body area networks (WBANs) as it is important to improve the lifetime of the network. WBAN routing is an effective approach for establishing energy efficiency sets and assign time slots for the network. Many algorithms that deal with interference model treats the whole WBAN as a minimum interference unit and increase their lifetime cycle. In this research, we report an effective low-energy adaptive clustering hierarchy (LEACH) routing protocol using MATLAB simulation and related C++ simulation codes to enhance the overall performance of the network by improving the energy efficiency and network lifetime cycles. Furthermore, the study sheds light up on the comparison of the protocol and proposes a modified protocol for WBAN. Based on the results obtained from conducting different configurations in the proposed design, the base station should be situated near the network to insure high network performance.
Assessing smart sustainable library practices in higher education: development and validation of instrument
Authors: Yunus Norhazura , Ismail Mohd Nasir
Published: June 2024

A smart sustainable library is a new form of a library that blends sustainability and smart libraries with an emphasis on ethics. This study focuses on the need for thorough tools to assess the evolving concept of a smart sustainable library, especially within Malaysian higher education. This study emphasizes the need for a comprehensive tool that combines smart library, sustainability practices, and ethical values in libraries. Developed and conducted a pilot study to validate a new instrument designed to assess these intertwined aspects thoroughly. By distributing a survey to 30 librarians from different academic institutions in Malaysia, we used statistical measures such as Cronbach's alpha, omega, and corrected item-total correlation to assess the validity and reliability of the instrument. The results showed a high level of reliability with Cronbach's alpha at 0.929 and Omega at 0.918, suggesting that the instrument has strong internal consistency and could be effective for wider use. Our research indicates that the newly developed instrument effectively captures the complex nature of smart sustainable libraries, demonstrating its potential for future research and practical use in the field. This research significantly contributes to the library science field by offering a validated tool to evaluate smart sustainable library development.
Revolutionizing malaria diagnosis: deep learning-powered detection of parasite-infected red blood cells
Authors: Hoque Md. Jiabul , Islam Md. Saiful , Khaliluzzaman Md. , Muntasir Abdullah Al , Mohsin Mohammad Abdullah Bin
Published: June 2024

Malaria is a significant global health issue, responsible for the highest rates of morbidity and mortality globally. This paper introduces a very effective and precise convolutional neural network (CNN) method that employs advanced deep learning techniques to automate the detection of malaria in images of red blood cells (RBC). Furthermore, we present an emerging and efficient deep learning method for differentiating between cells infected with malaria and those that are not infected. To thoroughly evaluate the efficiency of our approach, we do a meticulous assessment that involves comparing different deep learning models, such as ResNet-50, MobileNet-v2, and Inception-v3, within the domain of malaria detection. Additionally, we conduct a thorough comparison of our proposed approach with current automated methods for malaria identification. An examination of the most current techniques reveals differences in performance metrics, such as accuracy, specificity, sensitivity, and F1 score, for diagnosing malaria. Moreover, compared to existing models for malaria detection, our method is the most successful, achieving an accurate score of 1.00 in all statistical matrices, confirming its promise as a highly efficient tool for automating malaria detection.
Explaining transfer learning models for the detection of COVID-19 on X-ray lung images
Authors: Odeh Abd Al-Rahman , Mustafa Ahmad
Published: June 2024

Amidst the coronavirus disease 2019 (COVID-19) pandemic, researchers are exploring innovative approaches to enhance diagnostic accuracy. One avenue is utilizing deep learning models to analyze lung X-ray images for COVID-19 diagnosis, complementing existing tests like reverse transcription polymerase chain reaction (RT-PCR). However, trusting these models, often viewed as black boxes, presents a challenge. To address this, six explainable artificial intelligence (XAI) techniques: local interpretable model agnostic explanations (LIME), Shapley additive explanations (SHAP), integrated gradients, smooth-grad, gradient-weighted class activation mapping (Grad-CAM), and Layer-CAM are applied to interpret four transfer learning models. These models: VGG16, ResNet50, InceptionV3, and DenseNet121 are analyzed to understand their workings and the rationale behind their predictions. Validating the results with medical experts poses difficulties due to time and resource constraints, alongside the scarcity of annotated X-ray datasets. To address this, a voting mechanism employing different XAI methods across various models is proposed. This approach highlights regions of lung infection, potentially reducing individual model biases stemming from their structures. If successful, this research could pave the way for an automated system for annotating infection regions, bolstering confidence in predictions and aiding in the development of more effective diagnostic tools for COVID-19.
An overview of hand gesture recognition based on computer vision
Authors: Tasfia Rifa , Izzah Mohd Yusoh Zeratul , Binte Habib Adria , Mohaimen Tousif
Published: June 2024

Hand gesture recognition emerges as one of the foremost sectors which has gone through several developments within pattern recognition. Numerous studies and research endeavors have explored methodologies grounded in computer vision within this domain. Despite extensive research endeavors, there is still a need for a more thorough evaluation of the efficiency of various methods in different environments along with the challenges encountered during the application of these methods. The focal point of this paper is the comparison of different research in the domain of vision-based hand gesture recognition. The objective is to find out the most prominent methods by reviewing efficiency. Concurrently, the paper delves into presenting potential solutions for challenges faced in different research. A comparative analysis particularly centered around traditional methods and convolutional neural networks like random forest, long short-term memory (LSTM), heatmap, and you only look once (YOLO). considering their efficacy. Where convolutional neural network-based algorithms performed best for recognizing the gestures and gave effective solutions for the challenges faced by the researchers. In essence, the findings of this review paper aim to contribute to future implementations and the discovery of more efficient approaches in the gesture recognition sector.
Evaluation of machine learning and deep learning methods for early detection of internet of things botnets
Authors: Mashaleh Ashraf S. , Ibrahim Noor Farizah , Alauthman Mohammad , Al-karaki Jamal , Almomani Ammar , Atalla Shadi , Gawanmeh Amjad
Published: June 2024

The internet of things (IoT) represents a rapidly expanding sector within computing, facilitating the interconnection of myriad smart devices autonomously. However, the complex interplay of IoT systems and their interdisciplinary nature has presented novel security concerns (e.g. privacy risks, device vulnerabilities, Botnets). In response, there has been a growing reliance on machine learning and deep learning methodologies to transition from conventional connectivity-centric IoT security paradigms to intelligence-driven security frameworks. This paper undertakes a comprehensive comparative analysis of recent advancements in the creation of IoT botnets. It introduces a novel taxonomy of attacks structured around the attack life-cycle, aiming to enhance the understanding and mitigation of IoT botnet threats. Furthermore, the paper surveys contemporary techniques employed for early-stage detection of IoT botnets, with a primary emphasis on machine learning and deep learning approaches. This elucidates the current landscape of the issue, existing mitigation strategies, and potential avenues for future research.
Artificial intelligence for early-stage detection of chronic kidney disease
Authors: B Mamatha , Terdal Sujatha P
Published: June 2024

Early-stage detection of chronic kidney disease (CKD) is crucial in research to enable timely intervention, enhance understanding of disease progression, reduce healthcare costs and support public health initiatives. The traditional approaches on early-stage chronic kidney disease detection often suffer from slow convergence and not integrate advanced technologies, impacting their effectiveness. Additionally, security and privacy concerns related to patient data are ineffectively addressed. To overcome these issues, this research incorporates novel optimized artificial intelligence-based approaches. The main aim is to enhance detection process through enhanced hybrid mud ring network (EHMRN), a novel detection technique combining light gradient boosting machine and MobileNet, involving extensive data collection, including a large dataset of 100,000 instances. The introduced network is optimized through the mud ring optimization to attain enhanced performance. Incorporating spark ensures secure cloud-based storage, enhancing privacy and compliance with healthcare data regulations. This approach represents a significant advancement in primary stage detection more effectively and promptly. The results show that the introduced approach outperforms traditional approaches in terms of accuracy (99.96%), F1-score (99.91%), precision (100%), specificity (99.98%), recall (100%) and execution time (0.09 s).
Forecasting livestock feed sales using machine learning techniques: an analysis of the Moroccan market
Authors: Nebri Mohamed Amine , Moussaid Abdellatif , Bouikhalene Belaid
Published: June 2024

Agriculture, especially cereals, is important in sustaining economies and food security globally. This study delves into the Moroccan agricultural landscape, specifically focusing on predicting livestock feed sales to assist cereal company producers in optimizing production, streamlining supply chain operations, and enhancing customer satisfaction. Data collected from various markets across Morocco, including sales dates and locations, was combined with climate data and analyzed using advanced machine learning techniques, particularly the gradient boosting regression (GBR) algorithm, which achieved high accuracy with a mean absolute error (MAE) of 0.0203 and a root mean square error (RMSE) of 0.0281. The evaluation of multiple regression models revealed promising results, demonstrating the effectiveness of predictive models in accurately forecasting sales. These findings contribute valuable insights to sales forecasting in the cereal industry by considering weather conditions, production methods, and livestock-related variables, highlighting the importance of leveraging advanced machine learning techniques for optimizing production processes and meeting market demands efficiently in the agribusiness sector.
Employing educational data mining techniques to predict programming students at-risk of dropping out
Authors: B. Casillano Niel Francis , Cantilang Karen W.
Published: June 2024

This research primary aimed at evaluating various predictive models in predicting programming students at risk of dropping out. It also aimed at identifying attributes that are significant in predicting students at risk of dropping. The educational data mining process (EDM) was utilized as the research framework. The study conducted a ten-fold cross-validation, revealing that the k-nearest neighbors (kNN) algorithm achieved the highest classification accuracy at 95.5%. The decision tree model followed closely with a 94.9% accuracy, logistic regression exhibited 94.4%, and the neural network model yielded a classification accuracy of 93.2%. Further analysis, including confusion matrices and receiver operating characteristic (ROC) curves, provided detailed insights into the models' performance. Notably, the decision tree algorithm excelled in identifying students who did not drop out, with a misclassification rate of 9 out of 30 for dropped students. Analysis also showed that students’ assignments completed (AC), laboratory work (LW), and attendance (ATT) were the strongest predictors in identifying students at risk of dropping. Results of the study can be used by instructors to identify in advance student at risk of dropping and provide them with the necessary intervention to improve performance in programming.
Extracting geo-references from social media text using bi-long short term memory networks
Authors: Mangal Dharmendra , Makwana Hemant
Published: June 2024

The social media data provides great source of information about global and local events, with millions of users. More precisely, the fact that brief messages are practical and are highly popular. Many recent studies have been motivated to estimate the location of the events identified by tracking posts in social media text messages. It might be difficult to extract location data and estimate the location of an event while maintaining a sufficient level of situation awareness, particularly in disaster situations like fires or traffic accidents. In this presented work we proposed an approach to identify geo-references in the text messages. We used bi-directional long short term memory (LSTM) neural networks to extract location information in the text messages. The results show that applying Bi-LSTM on dataset gives high level accuracy after fine-tuning (up to 10 epochs). The testing results show that accuracy achieved is 0.98 and 0.076 loss value. This proves that the proposed methodology is better than the previous conditional random field (CRF)-based approaches.
Gesture recognition technology: a new dimension in human-computer interaction interface
Authors: Beisov Nurbol , Madyarova Gulnar , Kerimbayev Nurassyl
Published: June 2024

Development of an interface for intelligent gesture control to improve user experience and increase the efficiency of interaction with a computer. This paper proposes a gesture recognition system based on artificial intelligence using convolutional neural networks (CNN). The system comprises three stages: pre-processing, optimal frame determination, and gesture category identification. The extracted features used are independent of movement, scaling, and rotation, providing greater flexibility to the system. The suggested gesture control technology, known as Kazakh Sign Language (KSL) for Kazakh alphabets, eliminates the need for additional devices, enabling users to interact with the system naturally. Experiments demonstrated that the proposed KSL system can accurately recognize Kazakh language alphabet letters with a high precision of 97.3%, owing to the utilization of artificial intelligence and CNN to enhance the accuracy and effectiveness of gesture control. Gestures, a type of visual formation, are perceivable by computers through machine learning models. The selection of methods and systems for recognizing Kazakh sign language gestures was accompanied by addressing various challenges related to language-specific orthographic and gestural features. The developed gesture control interface for human-computer interaction is applied in the field of inclusive education, aiming to assist deaf and hard-of-hearing children in learning sign language.
Power allocation in NOMA using sum rate-based dwarf mongoose optimization
Authors: Thokala Chiranjeevi , Krishnan Karthikeyan Santhana , Erroju Hansika , Minipuri Sai Keerthi , Gouti Yogesh Kumar
Published: June 2024

The increasing number of consumers with diverse data rate needs is leading to increased heterogeneity in traditional cellular networks. Nonorthogonal multiple access (NOMA) has emerged as a promising method to serve a large number of users, but research shows that weak users (WU) and strong users (SU) have different throughputs. Intra-group interference reduces WUs throughput due to the superposition of signals. Improper power distribution impacts NOMA performance and lowers the total system rate. The multi-objective sum rate dwarf mongoose optimization algorithm (M-SRDMOA) is implemented as a solution to the NOMA network power allocation problems. The DMOA approach distributes adequate power to all NOMA users to increase the large sum rate. The effectiveness of the M-SRDMOA approach is supported by existing studies on fair NOMA scheduler (FANS) and multi-objective sum rate-based butterfly optimization algorithm (M-SRBOA). The M-SRDMOA’s potential sum rate with an SNR of 9dB and a noise variation=2 is 14.06 bps/Hz, which is high compared to M-SRBOA and FANS.
Image classification based on few-shot learning algorithms: a review
Authors: Qi Qiao , Ahmad Azlin , Ke Wang
Published: June 2024

Image classification is a critical task in the field of computer vision, and its importance has significantly increased over the past few years. Machine learning and deep learning techniques have demonstrated immense potential in this field. However, traditional image classification models require a vast amount of training data, which can be challenging and expensive to obtain. To overcome this limitation, researchers are turning to few-shot learning, which aims to classify images with limited training samples. This paper presents a detailed analysis of the field of image classification using few-shot learning. First, it investigates the use of data augmentation, transfer learning, and meta-learning methods in this field. Then, it introduces several commonly used datasets and evaluation metrics in few-shot classification, compares several classical few-shot classification methods, and summarizes the experimental results obtained from public datasets. Finally, this paper analyzes the current challenges in few-shot image classification and suggests potential future directions.
Semblanza de una docente de matemáticas En búsqueda de ambientes creativos para educar con alegría…
Authors: Manjarrés Nora Benítez
Published: June 2024

Este artículo presenta una connotación muy personal acerca de la manera en que podemos llegar a hacer más agradable la tarea de ser docentes de matemáticas. A través de él pretendo reflexionar sobre contextos, situaciones y eventos que han transformado mi práctica pedagógica, permitiéndome construir experiencias significativas con la premisa “Educar con alegría” a través de la utilización de diversos ambientes educativos que portan una visión integral del conocimiento.
He tomado como eje de este escrito mi propia experiencia de vida, pues estoy segura que otros docentes han experimentado situaciones muy parecidas a las mías, pero al igual que yo, no fueron conscientes de su importancia y trascendencia hasta que alguien o algo, logra hacernos recapacitar sobre cuáles son esos pequeños detalles y aspectos claves que permiten que las clases nos funcionen mucho mejor en ciertas oportunidades. Por esta razón, al terminar de escribir una situación clave, realicé una pregunta con la intención de que el lector, pueda hacer una re-lectura de su pasado, con la esperanza de ayudarle a develar algunas realidades que no había considerado y que potencialmente generarían cambios en su labor.
Ey! ¡mira! Camino y descubro mi ciudad Experiencia significativa desarrollada en la I.E Presbitero Antonio José Bernal Londoño S.J de Medellín 2008 – 2011
Authors: Aida Buitrago Doris
Published: June 2024

Este artículo está basado en la sistematización de la experiencia significativa ¡Ey! ¡Mira! Camino y descubro mi ciudad desarrollada con jóvenes del grado sexto en la institución educativa Presbitero. Antonio José Bernal, con el propósito de generar espacios pedagógicos y lúdicos que fortalezcan el sentido de pertenencia frente a la institución, su entorno y demás espacios de la ciudad. Comprende tres partes diferenciadas de la siguiente manera: la primera, ¡Ey!¡Mira!, describe el proceso inicial de esta experiencia significativa y la construcción colectiva del objetivo y planes de trabajo; la segunda, Camino y descubro, presenta la diferencia entre los conceptos de salida pedagógica, salida de entorno y salida de ciudad; por último, Mi ciudad, aborda los fundamentos teóricos que acompañaron el desarrollo de esta experiencia y la relación que presentan con el Modelo pedagógico de la institución.
Investigación e innovación matemática. El mundo de la matemática en el contexto de la naturaleza
Authors: Colonia Oscar
Published: June 2024

Este artículo muestra una experiencia que pretende lograr la interdisciplinariedad por medio de cuatro herramientas didácticas que se formulan con el propósito de generar una transformación de la acción en el aula. Es así como el dibujo, la relatoría, la maqueta y la autoevaluación, buscan generar la secuenciación, la diferenciación y la significación del saber matemático. La fundamentación de la experiencia se basa en la fenomenología, relacionando el conocimiento desde la vía cartesiana (plano cartesiano), la vía de la lógica (construcción de proposición), la vía psicológica (construcción del sujeto) y la vía del mundo de la vida (construcción de un contexto). La experiencia privilegia la red conceptual, el método hipotético-deductivo, la creatividad y promueve el poder de síntesis.
Construyendo caminos de paz y convivencia desde la primera infancia
Authors: Cecilia Lara Aura
Published: June 2024

Surgidos de la sentida necesidad comunitaria el Jardín Infantil y su Sede “Picardías Primaria” constituyen un modelo ejemplarizante de construcción pedagógica participativa. En ella el compromiso unificado de docentes y padres de familia, entorno a la idea de la educación digna y humanística para las niñas y los niños del sector, ha logrado crear un espacio ecológico y lúdico en el cual el conocimiento es una experiencia continua e integral de mejoramiento y crecimiento individual y colectivo, haciendo énfasis en los valores éticos, morales, espirituales y ciudadanos que deben prevalecer en una sociedad; abordando las problemáticas propias y sociales con actitud creativa y de consciencia ciudadana: “Construyendo caminos de paz y convivencia.”
Reflexión pedagógica sobre el proceso de escritura en el nivel de transición. Una experiencia significativa para el desarrollo de competencias comunicativas
Authors: Fernanda Rodríguez Maria
Published: June 2024

El propósito de este artículo es presentar una reflexión pedagógica a partir de una experiencia significativa: “La producción del texto expositivo: una secuencia didáctica para reflexionar sobre la escritura en el nivel de transición”. En esta experiencia participaron los estudiantes del grado transición del Colegio Integrado del Carare, municipio de Cimitarra, Santander. Fue diseñada, implementada y sistematizada por las docentes: María Clemencia Aristizábal, Claudia Osorio y María Fernanda Rodríguez.

El artículo realiza aportes a la comprensión del funcionamiento del sistema de escritura, promueve el desarrollo de competencias comunicativas, analiza y describe la propuesta como herramienta didáctica.
“Revista huellas”, letras que develan el alma. La lectura y la escritura, dos experiencias mágicas
Authors: Mari Salamanca Luz
Published: June 2024

“Huellas, letras que develan el alma”, es una experiencia pedagógica que tiene como propósito fortalecer el desarrollo de competencias y habilidades comunicativas, a través de estrategias que motivan a la comunidad educativa a asumir una posición analítica y propositiva desde el aula, frente a la literatura y su relación con el entorno. El ejercicio dialógico texto, contexto y lector, nutre la reflexión y el asombro de los participantes, generando lectores y escritores activos, capaces de confrontar su propia realidad y de proyectar opciones con sentido crítico en su cotidianidad a partir de la construcción de textos que se convierten en artículos publicados en cada edición de la revista.

El proyecto anterior surge de la voz de los estudiantes generando momentos de encuentro social y cultural en la comunidad, la región y el país. Parte de un diagnóstico escolar y soporte teórico de estándares y lineamientos curriculares del Ministerio de Educación Nacional en el área de castellano, como también del enfoque humanista y del aprendizaje significativo que propone la institución. Se retoman pedagogos como Daniel Goleman para formar al estudiante en el control y manejo de emociones y a Rosa Elvira Rico para la propuesta de modos básicos de interacción con los textos.
A suposta entronização de Alexandre Magno no Egito (332 a.C.): entre fragmentos de historiadores gregos, relatos helenístico-romanos e especulação historiográfica
Authors: Sant'Anna Henrique Modanez de
Published: June 2024

Comentadores do Romance de Alexandre (RA), contrapondo-se à reserva de historiadores modernos sobre o uso desse texto como fonte histórica para a vida do rei, frequentemente defendem o seu contrário. Isso é particularmente evidente no caso da suposta entronização de Alexandre em Mênfis. Tal manejo do RA como fonte para esse evento específico encontra guarida em biografias históricas e estudos recentes sobre os seus títulos reais egípcios, nos quais defende-se uma entronização menfita deduzida da utilização de protocolo onomástico completo em evidências arqueológicas do tempo do rei. Este artigo discute sistematicamente os relatos antigos sobre a sua primeira estadia em Mênfis com o intuito de destacar que a reserva de alguns historiadores modernos é fundamentada em razão do silêncio dos historiadores do tempo de Alexandre, cujas obras (preservadas em fragmentos) foram utilizadas pelos escritores do corpus principal de fontes helenístico-romanas. Defende que há indícios de uma estratégia macedônica para o alinhamento da sua experiência monárquica com tradições egípcias mais antigas, bem como da inclinação da elite sacerdotal menfita para a concretização de uma expectativa messiânica disseminada desde o desaparecimento de Nectanebo II. Ambas, no entanto, por razões que incluem o período da estadia de Alexandre no Egito e a imponente solenidade da coroação de reis egípcios, parecem não ter resultado em sua entronização formal em Mênfis. O silêncio das fontes helenístico-romanas permanece imperativo.
Uma Análise das Políticas Étnico-Raciais na Formação de Obstetrizes
Authors: Silva Milena Novais Oliveira , Campos Edemilson Antunes de
Published: June 2024

Despite being the majority in the country, the black population is exposed to various forms of racism on a daily basis. Among these, institutional racism, which can be understood as the failure of institutions to promote an adequate service, for ethnic-racial and cultural reasons, lacking assistance to this population, for example, with regard to access to health care services. Epidemiological data show that the black population has worse health indicators. Black women exemplify the concept of intersectionality, being susceptible to gender oppression, racism and socioeconomic inequalities, especially during the pregnancy-puerperal cycle, thus leading the rates of obstetric violence and maternal morbidity and mortality. The objective of this article is to analyze the training of midwives, based on ethnic-racial policies in education and health. Therefore, a qualitative-quantitative research was carried out, through an online questionnaire, which was applied to students and graduates of the Obstetrics Course, School of Arts, Sciences and Humanities, University of São Paulo (EACH-USP ). Based on the results collected, there was a need to expand the space for teaching ethnic-racial issues in health and, in particular, in the graduation of midwives. For this, the creation of a discipline was proposed: Ethnic-racial relations in health, which seeks to contribute to the graduation of health professionals capable of providing humanized assistance to the reproductive health of black women.
Análise das Experiências Administrativas das Federações Brasileiras no Programa de Consolidação do Pacto Nacional pela Gestão das Águas (PROGESTÃO)
Authors: Almeida Bianca Maria Jacinto de , Fontana Marcele Elisa , Almeida Natallya Levino de
Published: June 2024

PROGESTÃO is a financial incentive program, with the principle of payment for achieving targets defined between the National Water Agency and the federations of Brazil, which has taken on the task of promoting water resources public management in the country. However, federations often do not reach the established targets. Understanding the problems is essential to structure decision-making processes in order to improve processes. Thus, through document analysis, the recurring problems of non-compliance with the program's targets were surveyed. Based on this diagnosis, specialists in the area of water resources were consulted and mitigation actions were proposed for each problem. This work contributes to the joint analysis, helping public managers in proposing policies and actions aimed at making the program more efficient.
Forecasting the Economic Burden of Cardiovascular Disease and Stroke in the United States Through 2050: A Presidential Advisory From the American Heart Association
Authors: Kazi Dhruv S. , Elkind Mitchell S.V. , Deutsch Anne , Dowd William N. , Heidenreich Paul , Khavjou Olga , Mark Daniel , Mussolino Michael E. , Ovbiagele Bruce , Patel Sonali S. , Poudel Remy , Weittenhiller Ben , Powell-Wiley Tiffany M. , Joynt Maddox Karen E. ,   on behalf of the American Heart Association
Published: June 2024

BACKGROUND:

Quantifying the economic burden of cardiovascular disease and stroke over the coming decades may inform policy, health system, and community-level interventions for prevention and treatment.

METHODS:

We used nationally representative health, economic, and demographic data to project health care costs attributable to key cardiovascular risk factors (hypertension, diabetes, hypercholesterolemia) and conditions (coronary heart disease, stroke, heart failure, atrial fibrillation) through 2050. The human capital approach was used to estimate productivity losses from morbidity and premature mortality due to cardiovascular conditions.

RESULTS:

One in 3 US adults received care for a cardiovascular risk factor or condition in 2020. Annual inflation-adjusted (2022 US dollars) health care costs of cardiovascular risk factors are projected to triple between 2020 and 2050, from $400 billion to $1344 billion. For cardiovascular conditions, annual health care costs are projected to almost quadruple, from $393 billion to $1490 billion, and productivity losses are projected to increase by 54%, from $234 billion to $361 billion. Stroke is projected to account for the largest absolute increase in costs. Large relative increases among the Asian American population (497%) and Hispanic American population (489%) reflect the projected increases in the size of these populations.

CONCLUSIONS:

The economic burden of cardiovascular risk factors and overt cardiovascular disease in the United States is projected to increase substantially in the coming decades. Development and deployment of cost-effective programs and policies to promote cardiovascular health are urgently needed to rein in costs and to equitably enhance population health.
Integrative taxonomy sheds light on species boundaries and relationships in the inbred haplodiploid ambrosia beetle genus Xyleborus
Authors: LeMay Gabriel A. , Johnson Andrew J. , Hulcr Jiri
Published: June 2024

This study investigated the relationship between genetic and morphological evidence in a species complex of haplodiploid, near-inbred ambrosia beetles, which are also vectors of an invasive tree pathogen: Xyleborus ferrugineus, Xyleborus bispinatus, and Xyleborus impressus. The unorthodox genetic system may blur species boundaries and challenges the applicability of standard species concepts. Haplo-diploid scolytine systematics is rife with contentious species delimitation, but species limits have rarely been tested empirically. To test the congruence of morphology, genetics, and geography in species identity, we analyzed 16 morphological characters and two DNA barcodes (COI mtDNA and CAD DNA sequences) in samples from across Florida. Morphological characters used to distinguish species corresponded with phylogenetic relationships in the majority of individuals. The maximum intraspecific divergence for the COI barcoding region was up to 19%, among the largest reported for animals. The phylogenetic and morphological analyses suggest that the three species are valid, but the astonishing intraspecific genetic diversity suggests either additional cryptic species or pseudogenes.
A new species in the genus Circinaria (Lichenized Ascomycetes: Megasporaceae) from Pakistan
Authors: ZULFIQAR RIZWANA , HABIB KAMRAN , ASGHAR HAFIZA SIMAB , WAHAB HIRA , SOHRABI MOHAMMAD , PAUKOV ALEXANDER G. , REN QIANG , KHALID ABDUL NASIR
Published: June 2024

A crustose species Circinaria semicontorta is described here as new to science. It is characterized by a whitish areolate to subverrucose thallus with a thinning, cracked margin, not forming true lobes, areoles with raised whitish pseudocyphellae containing black ostioles of conidiomata, and long pycnoconidia, 12–25 μm. The phylogenetic analysis based on ITS sequences confirms placing the new species within Circinaria.
A new species of Anthurium (Araceae) from the canga vegetation of the Serra dos Carajás, Pará, Brazil
Authors: CAMELO MEL C. , COELHO MARCUS A.N.
Published: June 2024

A new species of Anthurium section Pachyneurium, A. pedrovianae sp. nov. is described and occurs in canga vegetation of Serra dos Carajás, Pará State, Brazil. Information on conservation status, taxonomy, habitat, geographic distribution, and phenology is provided. Morphologically, the new species is similar to A.lindmanianum but differs from that species peduncle > 3 times the size of the petiole and a spadix < 6 cm long., stipe 6.0–11.0 cm long., purple at the base at the junction with the peduncle, berries are entirely purple, obovate, brown, warty seeds. Additionally, an identification key to distinguish the Pachyneurium species present in Pará is included.
First record of the genus Chorinaeus Holmgren, 1858 (Hymenoptera: Ichneumonidae) from India and Thailand with the description of two new species
Authors: RANJITH A.P. , BUTCHER BUNTIKA A.
Published: June 2024

The metopiine genus, Chorinaeus Holmgren is reported for the first time from India and Thailand. We describe and illustrate two new species, C. amrithae Ranjith & Butcher (from India and Thailand) and C. femiae Ranjith & Butcher (from Thailand). A taxonomic key to the identification of the Oriental species of Chorinaeus is provided.
The Romance of Business
Authors: Gaston Bruce
Published: June 2024

Clovis’ aunt is talking to his about his cousin Sophie Chabhouse, who is extremely rich but also extremely mean. She tells him how Sophie was boasting about a beaver-skin stole that she had bought for a reduced price. His aunt tricks her by ordering a more expensive stole on approval and misrepresenting the price, to make Sophie think she has overpaid for hers. As a result, she sells her more expensive stole to Clovis’ aunt at a reduced price. The second stole is then returned to the store for a refund. Clovis commends his aunt for her business skills.
Mechanism Design in Sport Matches: Competitive Balance, Intermediate Targets, and Effort Elicitation in a Three-Stage Contest Game
Authors: Sanders Shane D.
Published: June 2024

In sport, various examples of intermediate match targets exist that advantage the target-achieving party’s match outcome. We examine said targets within paired, multi-stage Tullock contest games, one a nested contest with intermediate target and one without. In equilibrium, the aggregate match-effort maximizing League sets a substantial subsequent match advantage to the target-achieving party. The nested Tullock contest with intermediate target elicits higher aggregate match effort than any one-shot Tullock contest. The effect of intermediate targets is fully consistent with that of an aggregate effort eliciting mechanism employed by leagues. Extant empirical literature finds clear demand for such a mechanism.
Electroconvulsive therapy in a tertiary Australian mental health facility between 2009 and 2020
Authors: Martin Emily , Purushothaman Subramanian , Ballard Emma , Blake Julie A , Burke Kylie , Scott James G
Published: June 2024

Background:

Despite electroconvulsive therapy being one of the most effective treatments in psychiatry, few studies report trends in the provision of electroconvulsive therapy over time. This study aims to investigate the use of electroconvulsive therapy between 2009 and 2020 in an Australian public tertiary mental health facility, and to describe the electroconvulsive therapy patient population and change in courses of treatment.

Methods:

Routinely collected data for 677 patients who received 1669 electroconvulsive therapy courses of treatment at an Australian public tertiary mental health facility between 2009 and 2020 were examined.

Results:

The provision of acute electroconvulsive therapy was stable across the study period; however, the number of maintenance electroconvulsive therapy courses commenced declined over the study. Schizophrenia was the most common indication for index treatment (37.4%). The majority of patients (85.7%) received acute electroconvulsive therapy only. Voluntary provision of electroconvulsive therapy declined over the study period, reducing from 44.9% in 2009 to 16.3% in 2020.

Conclusion:

Over the study period, there was a significant reduction in the number of maintenance electroconvulsive therapy courses commenced, and a large increase in involuntary treatment. The provision of electroconvulsive therapy was more likely to occur in males with a diagnosis of schizophrenia. Further studies are needed to generate a greater understanding of the factors influencing the provision of electroconvulsive therapy within differing geographical, social and healthcare landscapes.
When the Spotlight Burns: Gender Bias in the Public Perception of Entrepreneurs
Authors: Titus Varkey K. , O’Brien Jonathan P. , Parker Owen , Aumueller Christopher
Published: June 2024

We examine the interface of entrepreneurship and society by considering a novel source of gender bias (public opinion) and a novel expression of it (affective evaluations). We posit that women-led teams displaying success will trigger a “penalty for success” bias, and this will be inhibited if the team receives a “stamp of approval” from a gender congruent individual (i.e., an investor who is a man). Analysis from our first study, based on archival data, indicated that other mechanisms might be at play that we could not tease apart in our archival data, so we conducted a follow-up experimental study to further examine our initial findings. Results jointly indicate that women are viewed unfavorably unless either (a) they are validated by success or endorsement from a man, or (b) they fail but are “warm and accommodating” about it. We also find that while men benefit from being validated by success or endorsement of an opposite-gender investor, they do not face a similarly steep penalty absent that validation.
Providing Health Care and Social Support during Economic Crises: Lessons Learned from “Solidarity Outpatient Clinics” in Greece during the Great Recession
Authors: Matsaganis Matthew D. , Petraki Maria , Karanatsiou Vassia
Published: June 2024

“Solidarity outpatient clinics” (SOCs) emerged in Greece as a novel community-based health care resource during the global economic crisis that started in 2008. They have provided crucial social support to diverse vulnerable populations. Solidarity is a critical organizational principle underlying SOCs’ operation. It is juxtaposed to charity to emphasize, among other things, building symmetrical relationships between providers and patients. Employing a case study approach and a multilevel, multimethod research design, we analyzed qualitative data collected through semistructured interviews (N = 20) with patients, staff, and other local stakeholders and content of monthly informational bulletins (N = 26) and weekly radio shows (N = 48) produced by a prominent SOC in Greece’s capital. Findings provide insight into structural and functional dimensions of social support exchanges at SOCs and extend our understanding of different types of social support and the organizational contexts through which they are secured, particularly during financial crises.
Feeding cattle under suboptimal conditions in Kenya: From emphasising technical (non-)adoption to stimulating adaptive performance
Authors: Vernooij Vera , Koeijer Jan de , Vellema Sietze , Crane Todd , Maiyo Nathan
Published: June 2024

In the climate-development interface, research for development gained a strong interest in climate-smart agriculture and sustainable forms of intensification. Moreover, the urgency to respond to climate change stimulates a strong and sometimes exclusive focus on mitigation, driven by research-based agricultural practices. In the livestock sector, this materialises in strategies to lower emissions from livestock, which centralises the adoption question: what can we do to stimulate the use of best practices by smallholder farmers? This paper flags the risk that this outlook may overlooksmallholder farmers' capacities to navigate sub-optimal conditions of drought or scarcity. The paper applies an alternative lens for understanding ‘agriculture-as-performance’ and highlights smallholder cattle owners’ agency and resourcefulness. It aims to create a conceptual space to examine how adaptive capacities are grounded in the rhythms of agriculture under suboptimal conditions. Empirically, the study focuses on the cattle-feeding practices of five farming households evolving through seasons and lifetimes in a sedentary agricultural system in Kenya. Our frame identifies diverse affordances, conceptualised as opportunities for actions, emerging and disappearing in the immediate material environment, and we portray cattle feeding as a networked response anchored in practices of giving, sharing, and receiving. We use our insights into the dynamics and performance of entangled socio-technical practices to sketch the contours of an alternative pathway for agriculture for development. We argue in favour of a shift from an exclusive focus on the adoption of predefined optimal solutions to a diagnostic and catalytic approach integrating situated adaptive performances through which farming households respond to action opportunities.
The Dark Side of Safety: A Call for a More Thorough Consideration of Racism and Collective Power Motivations in the Social Psychology of Firearms
Authors: Higginbotham Gerald D.
Published: June 2024

This paper situates current social psychological research on the symbolic use of firearms (e.g., as a source of personal safety) in broader historical context to motivate a more thorough consideration of collective power motives. Historically, firearms have been used to dominate racial outgroup members (e.g., White Americans use of firearms and firearm laws to dispossess indigenous people of land or control free and enslaved Black people) or, at times, attempt to resist group-based oppression (e.g., Black Americans use of firearms to struggle against White Jim Crow terrorism). Given most gun owners report self-protection as their primary reason for firearm ownership and yet anti-Black attitudes are still a consistently important predictor of firearm ownership among dominant group members (e.g., White Americans), this paper examines how guns may function as a perceived source of personal safety and collective power. I center the persistent role of White supremacy and anti-Blackness in original U.S. firearm psychology and policy to illuminate the interrelatedness of personal safety and collective power perceptions, and how perceived threats to in-group power may motivate the use of guns and policies that selectively regulate gun access to mitigate associated safety concerns. Seeking to nudge social psychology to more thoroughly examine firearms’ potential function as a symbolic source of collective power, I end by discussing how considering collective power can help us better understand how historically dominant and historically marginalized groups view firearms today while also illuminating some barriers to the pursuit of gun safety for all.