bluuebunny/crossref_metadata_2025 huggingface dataset:
JointNet4BCD: A Semi-Supervised Joint Learning Neural Network with Decision Fusion for Building Change Detection
Authors: Chen Hao , Sun Chengzhe , Li Jun , Du Chun
Published: December 2024

Remote sensing image building change detection aims to identify building changes that occur in remote sensing images of the same areas acquired at different times. In recent years, the development of deep learning has led to significant advancements in building change detection methods. However, these fully supervised methods require a large number of bi-temporal remote sensing images with pixel-wise change detection labels to train the model, which incurs substantial time and manpower for annotation. To address this issue, this study proposes a novel single-temporal semi-supervised joint learning framework for building change detection, called JointNet4BCD. Firstly, to reduce annotation costs, we design a semi-supervised learning manner to train our model using a small number of building extraction labels instead of a large amount of building change detection labels. Furthermore, to improve the semantic understanding capability of the model, we propose a joint learning approach for building extraction and change detection tasks. Lastly, a decision fusion block is designed to fuse the building extraction results into the building change detection results to further improve the accuracy of building change detection. Experimental results on the two widely used datasets demonstrate that the proposed JointNet4BCD achieves excellent building change detection performance while reducing the need for labels from thousands to dozens. Using only ten labeled images, JointNet4BCD achieved F1-Scores of 83.93% and 83.45% on the LEVIR2000 and WHU datasets, respectively.
Real-Time Radar Classification Based on Software-Defined Radio Platforms: Enhancing Processing Speed and Accuracy with Graphics Processing Unit Acceleration
Authors: Oncu Seckin , Karakaya Mehmet , Dalveren Yaser , Kara Ali , Derawi Mohammad
Published: December 2024

This paper presents a comprehensive evaluation of real-time radar classification using software-defined radio (SDR) platforms. The transition from analog to digital technologies, facilitated by SDR, has revolutionized radio systems, offering unprecedented flexibility and reconfigurability through software-based operations. This advancement complements the role of radar signal parameters, encapsulated in the pulse description words (PDWs), which play a pivotal role in electronic support measure (ESM) systems, enabling the detection and classification of threat radars. This study proposes an SDR-based radar classification system that achieves real-time operation with enhanced processing speed. Employing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a robust classifier, the system harnesses Graphical Processing Unit (GPU) parallelization for efficient radio frequency (RF) parameter extraction. The experimental results highlight the efficiency of this approach, demonstrating a notable improvement in processing speed while operating at a sampling rate of up to 200 MSps and achieving an accuracy of 89.7% for real-time radar classification.
A Low-Cost Flexible Optoelectronic Synapse Based on ZnO Nanowires for Neuromorphic Computing
Authors: Yue Yongqing , Yu Zixia , Li Fangpei , Peng Wenbo , Zhu Quanzhe , He Yongning
Published: December 2024

Neuromorphic computing, inspired by the brain, holds significant promise for advancing artificial intelligence. Artificial optoelectronic synapses, which can convert optical signals into electrical signals, play a crucial role in neuromorphic computing. In this study, we successfully fabricated a flexible artificial optoelectronic synapse device based on the ZnO/PDMS structure by utilizing the magnetron sputtering technique to deposit the ZnO film on a flexible substrate. Under UV light illumination, the device exhibits excellent synaptic plasticity, including excitatory postsynaptic current (EPSC), short-term potentiation (STP), and paired-pulse facilitation (PPF). By growing ZnO nanowires, we improved the fabrication processes and further enhanced the synaptic properties of the device, demonstrating long-term potentiation (LTP) and the transition from short-term memory (STM) to long-term memory (LTM). Additionally, the device exhibits outstanding flexibility, maintaining stable synaptic plasticity under bending conditions. This device shows broad application potential in mimicking visual systems and is expected to contribute significantly to the development of neuromorphic computing.
Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks
Authors: Colonnese Federica , Di Luzio Francesco , Rosato Antonello , Panella Massimo
Published: December 2024

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and repetitive behaviors, often associated with atypical visual attention patterns. In this paper, the Gaze-Based Autism Classifier (GBAC) is proposed, which is a Deep Neural Network model that leverages both data distillation and data attribution techniques to enhance ASD classification accuracy and explainability. Using data sampled by eye tracking sensors, the model identifies unique gaze behaviors linked to ASD and applies an explainability technique called TracIn for data attribution by computing self-influence scores to filter out noisy or anomalous training samples. This refinement process significantly improves both accuracy and computational efficiency, achieving a test accuracy of 94.35% while using only 77% of the dataset, showing that the proposed GBAC outperforms the same model trained on the full dataset and random sample reductions, as well as the benchmarks. Additionally, the data attribution analysis provides insights into the most influential training examples, offering a deeper understanding of how gaze patterns correlate with ASD-specific characteristics. These results underscore the potential of integrating explainable artificial intelligence into neurodevelopmental disorder diagnostics, advancing clinical research by providing deeper insights into the visual attention patterns associated with ASD.
Simultaneous Multibeam Clustered Phased Arrays Analysis Using Mixed and Multiple Antenna Element Factors
Authors: Dicandia Francesco Alessio , Genovesi Simone
Published: December 2024

A novel design strategy for improving the radiative performance of simultaneous multibeam (SMB) phased arrays is addressed. The proposed scheme relies on the adoption of mixed and multiple antenna element factors with a dynamic selection of their radiation patterns whose choice depends on the desired SMB pointing directions. In addition, a Penrose-inspired clustering technique is also employed for reducing the array feed points. Compared with traditional phased arrays based on a single antenna element factor, the novel array architecture allows the scan angle range to be widened by improving the minimum array gain as well as reducing the peak side lobe level (PSLL). The superior radiative performance of the proposed approach with respect to the clustered phased arrays with a single-mode element factor is assessed in SMB scenarios comprising two and three main lobe peaks. The notable SMB radiative improvement has been also confirmed from a statistical point of view by considering up to four and five concurrent main lobes. The remarkable radiative improvements confirm the effectiveness of the proposed solution, which also represents an appealing candidate for its exploitation in multiuser and multibeam communications.
Numerically Simulated Asphalt Thermal Maps for More Sustainable Road Management
Authors: García-Ros Gonzalo , Sánchez-Pérez Juan Francisco , Salazar-Ros Jose Joaquín , Oviedo-Casado Santiago , García-Rabadán Victor , Motos-Cascales Gloria
Published: December 2024

Road construction and maintenance are crucial for a functioning society, and achieving cost-effective and environmentally sustainable practices requires a deep understanding of road degradation processes. Temperature fluctuations and extreme temperatures are significant contributors to road surface deterioration, leading to issues such as cracking, rutting, and deformation, which not only reduce the road lifespan but also release fine particulate matter, posing environmental hazards. In this study, we develop detailed road surface temperature maps for Spain, using real-time weather and solar irradiation data from 46 evenly distributed meteorological stations. These maps provide hourly average road surface temperatures for each season and highlight extreme conditions during summer and winter, incorporating meteorological variables such as air temperature, wind speed, and relative humidity, showing that during winter, but particularly during summer, most of the territory is subjected to extreme temperatures. By visualizing thermal stress on road surfaces, the road surface temperature maps enable the identification of high-risk areas, supporting targeted maintenance, conservation efforts, and pollution mitigation, ultimately contributing to a more sustainable road infrastructure.
What Does SDG Monitoring Practice Tell Us? An Analysis of 120 Voluntary Local Reviews
Authors: Stamos Iraklis , Vivas Luis , Enrique Regueira Iria , Bertozzi Cecilia
Published: December 2024

This paper examines how local governments monitor the Sustainable Development Goals (SDGs) through voluntary local reviews (VLRs), focusing on localised practices from 2016 to 2024. Given the crucial role of local authorities in SDG implementation, the research aims to analyse differences in monitoring approaches and indicator usage across various types of local governments worldwide. The analysis involved 120 VLRs, using both quantitative methods to assess the number of indicators per goal and semantic clustering techniques to explore thematic patterns. Results show that cities are the most active in SDG localisation, especially for goals aligned with urban governance, such as Goal 11 (Sustainable Cities) and Goal 4 (Quality Education). Some goals, such as Goal 15 (Life on Land) and Goal 17 (Partnerships), are less frequently monitored, reflecting challenges in translating national objectives to local contexts. Semantic clustering highlighted both strong alignments and gaps in SDG indicator usage, particularly for overlapping goals. The paper underscores the need for more tailored, context-specific indicators for local authorities to effectively monitor SDGs, while highlighting the leadership role that cities play in this process.
Optimising Forest Management Using Multi-Objective Genetic Algorithms
Authors: Castro Isabel , Salas-González Raúl , Fidalgo Beatriz , Farinha José Torres , Mendes Mateus
Published: December 2024

Forest management requires balancing ecological, economic, and social objectives, often involving complex optimisation problems. Traditional mathematical methods struggle with these challenges, leading to the adoption of metaheuristic approaches like the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This paper introduces a custom NSGA-II algorithm, incorporating a specialised mutation operator to enhance solution generation for multi-objective forest planning. The custom NSGA-II is compared to the standard NSGA-II in a scenario aiming to maximise timber harvest volume and minimise its standard deviation, with a minimum volume constraint. Key performance metrics include non-dominated solutions, spacing, computational cost, and hypervolume. The results demonstrate that the custom NSGA-II provides more valid solutions and better explores the solution space. This approach offers a user-friendly and efficient tool for forest managers, integrating well with Web-based systems for modern, sustainability-oriented forest planning.
Optimal Routing and Sustainable Operation Scheduling of Large Ships with Integrated Full-Electric Propulsion
Authors: Koumaniotis Epameinondas K. , Kanellos Fotios D.
Published: December 2024

The continuously intensified pursuit to reduce emissions related to human activity and the increased competition in maritime sector calls for sustainable and well-planned solutions to conform with environmental constraints and maximize profit, respectively. In a sector that is very critical for human activities, such as the maritime industry, it is essential to be able to reduce ship emissions without increasing the overall cost of operations and the time to transfer the cargo. All these parameters make ship routing and ship emission reduction very crucial. This work examines the effective routing of large ships with an integrated full-electric propulsion system and the optimal power generation scheduling of their generators to attain the minimum possible operational cost. To achieve this, the problem was formulated, modeled and solved in two stages, namely, ship routing and power generation scheduling, respectively. The first stage was solved using the Particle Swarm Optimization (PSO) method and the second one with a conventional optimization algorithm based on the steepest decent concept. The proposed ship routing method is based on the sea resistance concept and the minimization of total ship propulsion energy. The obtained results show that the optimal path is a combination of the minimum distance path and the minimum resistance path. Ship sustainability is reinforced with the reduction in ship operation cost and ship emissions. Ship emission reduction is achieved in the second optimization stage using a suitable emission index that complies with IMO regulations.
Evaluation of Ecological Rehabilitation Models for Abandoned Mines Using Analytic Hierarchy Process–Entropy Method and Sustainable Development Strategies
Authors: Xia Siyuan , Yang Wei , Tan Shucheng
Published: December 2024

This study focuses on the ecological restoration of abandoned mines to develop an evaluation model and propose policy recommendations. The research highlights that while mining contributes to economic growth, it also brings numerous abandoned mines and associated environmental issues. Western countries initiated research on abandoned mines earlier, while China continues to explore various restoration approaches. This paper, adhering to the principles of scientific validity, employs the analytic hierarchy process (AHP) and entropy method to construct an evaluation indicator system. Seven indicators were selected, and their weights were determined using both methods to derive the combined weights. The results indicate that factors such as the water source conditions significantly influence the selection of restoration methods, with different approaches yielding varied evaluation scores. Based on this foundation, sustainable development strategies are proposed for development models regarding composite tourism development, geopark development, and ecological regreening. These strategies include enhancing resource utilization efficiency, strengthening ecological protection, and promoting the transformation of ecological value, etc. The aim is to align the rehabilitation of abandoned mines with the goal of sustainable development, offering a scientific basis and policy guidance for the ecological rehabilitation of mines while fostering the sustainable development of the region.
Characteristics of Spatial–Temporal Evolution of Sustainable Intensification of Cultivated Land Use and Analysis of Influencing Factors in China, 2001–2020
Authors: Liu Guiying , Yang Mengqi
Published: December 2024

The rapid growth of the global population, the acceleration of the urbanization process, and the demands of economic development, place enormous pressure on scarce land resources. Cultivated land use presents a series of problems, hindering its socioeconomic and ecological sustainability. The sustainable intensification of cultivated land use (SICLU) is a development model designed to maximize land use efficiency, while minimizing environmental pollution. It is considered to be an efficient method to achieve three aspects of sustainable goals, namely in regard to society, the economy, and ecology, simultaneously. This approach has significant theoretical and practical implications for China’s food security and ecological safety. This study incorporates the “agricultural carbon emissions” indicator into the indicator evaluation system. Using the super-efficiency SBM model, we estimate the SICLU levels in China from 2001 to 2020. ArcGIS and the Dagum Gini coefficient decomposition model are employed to explore the temporal and spatial evolution characteristics and non-equilibrium spatial dynamics of SICLU in China. Finally, the Tobit regression model is used to reveal the driving factors. The results show the following: (1) Since 2003, China’s SICLU levels demonstrate an overall ascent amid fluctuations, sustaining a relatively high average annual level of 0.945. (2) In terms of spatial evolution patterns, China’s SICLU levels demonstrate significant spatial disparities, with distinct differences among the four major regions. Regions with similar SICLU levels show a certain degree of spatial adjacency. (3) There are significant regional disparities in China’s SICLU levels, which overall exhibit a declining trend. The differences between regions are the primary source of spatial variation, followed by hypervariable density and intra-regional disparities. (4) The regional industrial structure, the level of agricultural modernization, the agricultural cropping structure, and the per capita sown area, positively influence the enhancement of SICLU levels in China. Throughout the study period, the SICLU levels in China continuously improved and the overall regional disparities diminished. However, significant inter-regional imbalances persist, necessitating tailored optimization measures, based on local conditions. Establishing a coordinated mechanism for orderly and synergistic regional development is crucial, in order to provide references to decision-makers to promote the rational use of arable land in China.
Towards Sustainable Material Design: A Comparative Analysis of Latent Space Representations in AI Models
Authors: Casado Ulises Martin , Altuna Facundo Ignacio , Miccio Luis Alejandro
Published: December 2024

In this study, we employed machine learning techniques to improve sustainable materials design by examining how various latent space representations affect the AI performance in property predictions. We compared three fingerprinting methodologies: (a) neural networks trained on specific properties, (b) encoder–decoder architectures, and c) traditional Morgan fingerprints. Their encoding quality was quantitatively compared by using these fingerprints as inputs for a simple regression model (Random Forest) to predict glass transition temperatures (Tg), a critical parameter in determining material performance. We found that the task-specific neural networks achieved the highest accuracy, with a mean absolute percentage error (MAPE) of 10% and an R2 of 0.9, significantly outperforming encoder–decoder models (MAPE: 19%, R2: 0.76) and Morgan fingerprints (MAPE: 24%, R2: 0.6). In addition, we used dimensionality reduction techniques, such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE), to gain insights on the models’ abilities to learn relevant molecular features to Tg. By offering a more profound understanding of how chemical structures influence AI-based property predictions, this approach enables the efficient identification of high-performing materials in applications that range from water decontamination to polymer recyclability with minimum experimental effort, promoting a circular economy in materials science.
Integrating Greenhouses into Buildings: A Renewed Paradigm for Circular Architecture and Urban Regeneration
Authors: D’Ostuni Michele , Zou Tong , Sermarini Allison , Zaffi Leonardo
Published: December 2024

In the post-COVID-19 era, there has been an increasing interest in re-evaluating citizens’ living conditions within dense and grey urban areas. The provision of green spaces has always been identified as an important aspect of alleviating contemporary everyday life stress and preventing or limiting mental health-related issues. It is also an important strategy to mitigate urban heat islands and foster adaptation strategies to climate change. Among the numerous experiments of ‘green action’ available to urban planners, urban farming strategies have been widely used in Europe to provide green spaces and ecosystem services, exploring the topics related to self-production of food, biodiversity, and zero-km cultivation. Therefore, finding new spaces for agriculture in urban environments has driven scientists, researchers, and entrepreneurs to develop new soilless technologies (such as hydroponics, aquaponics, and aeroponics) to maximize yields in urban areas, creating new agricultural and architectural models such as the vertical farms (VF) and the building-integrated greenhouses (BIGH). In this regard, the objective of this paper is to recontextualize the integrated greenhouse element for high-tech food production as new iconic architectural models derived from the experience of the Victorian Winter Gardens and the first tropical greenhouses. Revisiting these perspectives, this paper offers opportunities to redefine the greenhouse as a multifunctional asset that aligns with both environmental goals and architectural standards.
Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks
Authors: Leontiou Theodoros , Frixou Anna , Charalambides Marios , Stiliaris Efstathios , Papanicolas Costas N. , Nikolaidou Sofia , Papadakis Antonis
Published: December 2024

Background: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. Methods: In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. The network’s performance was evaluated under both ideal and non-ideal conditions, incorporating noise and background temperature variations. A physics-informed loss function embedding the heat equation was used in conjunction with statistical uncertainty during training to simulate realistic scenarios. Results: The CNN achieved high accuracy for small phantoms (e.g., 10 cm in diameter). However, under non-ideal conditions, the network’s predictive capacity diminished in larger domains, particularly in regions distant from the surface. The introduction of physical constraints in the training processes improved the model’s robustness in noisy environments, enabling accurate reconstruction of hot-spots in deeper regions where traditional CNNs struggled. Conclusions: Combining deep learning with physical constraints provides a robust framework for non-invasive thermal imaging and other applications requiring high-precision temperature field reconstruction, particularly under non-ideal conditions.
Water Balance Analysis in the Majalaya Watershed: Two-Step Calibration and Application of the SWAT+ Model for Low-Flow Conditions
Authors: Kardhana Hadi , Lihawa Abdul Wahab Insan , Rohmat Faizal Immaddudin Wira , Wulandari Siska , Harjupa Wendi , Adiprawita Widyawardana , Kardena Edwan , Kusuma Muhammad Syahril Badri
Published: December 2024

Understanding hydrological processes is crucial for effective watershed management, with SWAT+ being one of the widely adopted models for analyzing water balance at watershed scales. While hydrological components are often assessed through sensitivity analysis, calibration, and validation, parameter sensitivity during dry periods (low-flow conditions) when baseflow is predominant remains a relevant focus, especially for watersheds like Majalaya, Indonesia, which experience distinct low-flow periods. This study analyzes water balance components in the Majalaya watershed, Indonesia, using SWAT+ across the 2014–2022 period, focusing on low-flow conditions. This study employs a two-step calibration approach using various datasets, including ground rainfall (2014–2022), NASA POWER meteorological data, MODIS land cover, DEMNAS terrain, and DSMW soil types, and the streamflow data for model calibration. The first calibration step optimized the overall performance (R2 = 0.41, NSE = 0.41, and PBIAS = −7.33), and the second step improved the baseflow simulation (R2 = 0.40, NSE = 0.35, and PBIAS = 12.45). A Sobol sensitivity analysis identified six primary parameters, i.e., CN3_SWF, CN2, LATQ_CO, PERCO, SURLAG, and CANMX, as the most influential for streamflow calibration, with CN3_SWF and CN2 being the most critical. This study demonstrates SWAT+’s effectiveness in watershed management and water resource optimization, particularly during low-flow conditions.
Cenário sociodemográfico de pacientes com Hanseníase no Estado do Pará, Brasil no período de 2013 a 2022
Authors: Melo Gustavo Antonio Silva de , Santos Caio Murilo da Costa , Silva Sarah Jany Correa da , Silva Bruno José Martins da , Torres Maria Karoliny da Silva
Published: December 2024

A hanseníase é uma doença infectocontagiosa crônica, causada pelo bacilo Mycobacterium leprae, que afeta a pele e os nervos periféricos (células de Schwann). Reconhecida como um problema de saúde pública, a hanseníase atinge todos os continentes. Nesse contexto, este trabalho teve como objetivo descrever o perfil sociodemográfico dos casos de hanseníase no Estado do Pará, abrangendo o período de 2013 a 2022, e analisando sua distribuição e principais características epidemiológicas. Trata-se de um estudo quantitativo do tipo observacional, descritivo e transversal, que avaliou a distribuição da doença na população com base nos dados do Sistema de Informação de Agravos de Notificação (SINAN), acessados por meio do Departamento de Informática do Sistema Único de Saúde (DATASUS). Durante o período do estudo, foram notificados 32.377 casos da doença, com a maioria ocorrendo na faixa etária de 20 a 59 anos (66,32%), predominantemente no sexo masculino (63,10%), entre a população parda (74,65%) e com ensino fundamental incompleto (58,56%). A classificação operacional com maior incidência foi a multibacilar (76,26%), sendo a forma clínica dimorfa a mais notificada (53,96%). A população com maior prevalência de casos compreende indivíduos da faixa etária economicamente ativa, predominantemente do sexo masculino, autodeclarados pardos e com baixo nível de escolaridade. Portanto, pesquisas como esta são essenciais para entender o comportamento da hanseníase na população, contribuindo para a formulação de políticas públicas, além de aprimorar a prevenção, o diagnóstico precoce, a adesão ao tratamento e o conhecimento sobre a doença.
Uso de substâncias psicoativas entre os enfermeiros que estão/estiveram na linha de frente da COVID-19
Authors: Coelho Laís Mirela Ferro , Leite Mylena Gabriella de Souza , Lacerda Laíra Gomes , Iocca Diego Cesar , Sampaio Julliane Messias Cordeiro , Bampi Luciana Neves da Silva , Oliveira Ester Mascarenhas , Pegoraro Vanessa Alvarenga
Published: December 2024

Substância psicoativa é um termo científico contemporâneo para definir os compostos, extratos, plantas, pílulas, bebidas, pós, gases, e qualquer substrato que contenham moléculas às quais possuem propriedades de alterar a fisiologia do sistema nervoso, a percepção ou a consciência humana. O uso de substâncias entre enfermeiros é um problema que ameaça os padrões profissionais e a prestação de um cuidado de qualidade aos pacientes. Se comparado com a realidade pandêmica, houve aumento da carga horária, escassez quanto aos insumos e consequentemente, maior risco de contaminação pelas variantes do coronavírus, dentre outros desafios. Objetivou-se identificar o uso/abuso de substâncias psicoativas entre os enfermeiros (as) que atuam/atuaram na linha de frente contra a COVID-19. Tratou-se de um estudo transversal, descritivo, de abordagem quantitativa. O público-alvo da presente pesquisa foram 25 enfermeiros, trabalhadores de um hospital universitário do Distrito Federal. Os enfermeiros entrevistados foram questionados quanto ao desenvolvimento de algum tipo de transtorno mental durante a atual pandemia pela COVID-19, e 32% deles responderam de forma afirmativa. Também foram questionados quanto ao uso de substâncias psicoativas, 56% dos enfermeiros confirmaram o uso. Já quanto ao uso de álcool, obteve-se 24% das respostas afirmativas, seguido de 8% para uso de ansiolíticos e para psicotrópicos e 16% para antidepressivos. 32% dos entrevistados informaram que o uso se iniciou durante a pandemia de COVID-19. Apesar das limitações evidenciadas neste estudo, como uma baixa amostra de participantes e o estigma sobre o tema abordado, foi possível compreender os fatores de risco que podem levar o profissional Enfermeiro ao uso destas substâncias. Sendo eles: tempo de descanso reduzido, jornadas de trabalho longas e exaustivas, baixa remuneração e valorização profissional, preocupação com sua contaminação e de seus familiares, vivência constante com o sofrimento humano e morte.
Avaliação da analgesia pós-operatória por bloqueio paravertebral, plano eretor da espinha e peridual em toracotomias: uma revisão sistemática
Authors: Carvalho Eduardo Longo Correia de , Silva Christiane Rodrigues da , Vieira Thatiana Lucia Cintra de Alcantra
Published: December 2024

A toracotomia é um procedimento cirúrgico invasivo, e devido o grande trauma cirúrgico que causa nos músculos, fáscia e outros tecidos durante o procedimento toracotomia, a dor observada nos pacientes tanto no pós-operatório imediato quanto no tardio, constitui uma preocupação constante na prática cirúrgica. Dentre as opções apresentadas pela literatura científica, o uso de bloqueio peridural, bloqueio paravertebral, e bloqueio do plano eretor da espinha podem favorecer uma melhor e mais rápida recuperação e menor taxa de readmissão hospitalar, reduzindo o consumo de opióide e os efeitos adversos. Ante ao exposto, esclarece-se que este estudo tem por objetivo realizar uma análise revisar a literatura cientifica atual a que trata a temática da analgesia pós-operatória por meio de bloqueio dos nervos paravertebral, plano eretor da espinha e peridual após procedimento de toracotomia. Este estudo desenvolveu-se por meio de uma revisão sistemática desenvolvida nas bases de dados US National Library of Medicine National Institutes of Health e Scientific Electronic Library Online. Incluiu-se estudos publicados nos últimos 5 anos, e nos idiomas português e inglês. Inicialmente foram identificados 280 estudos envolvendo a temática, e após análise mais detalhada 17 estudos foram elegíveis envolvendo a temática para análise e inclusão no escopo desta revisão. A maioria foram publicados nos anos de 2022, no idioma inglês, guiando-se por metodologias de relato de caso e estudos randomizados. A utilização dos bloqueios paravertebral, peridural e do plano eretor da espinha foi comprovado, com base na literatura científica investigada, como benéficos para analgesia de dor aguda ou crônica pós toracotomia, favorecendo ainda a redução da administração de opioides e seus efeitos colaterais, minimizando as taxas de complicações pós-operatórias e tempo de internação hospitalar.
Perfil epidemiológico da neoplasia maligna da pele no Brasil nos anos de 2018 a 2023
Authors: Silva Lucas Rocha Santana da , Lobo Gustavo Soares , Costa Alexandre Marinho , Costa Alexandre Marinho , Arruda Fernando Damasceno , Barros Katia Talita dos Santos , Teixeira Luciano de Castro Rezende Araujo , Trevilato Pedro Victor Rocha , Silva Marlon Maia da , Silva Maycon Maia da , Melo Lucas Ferreira , Oliveira Roberta Beatriz Santos , Rocha Fabiane Holanda Batista Porfírio da
Published: December 2024

Introdução:As neoplasias malignas da pele referem-se a crescimento anormal e desordenado de células da pele devido a fatores como radiação solar , predisposição genética e outros fatores carcinogênicos. Os tipos mais comuns são o carcinoma basocelular e espinocelular , sendo o menos prevalente o câncer de pele melanoma. Nesse contexto é importante entender o perfil da doença a fim de desenvolver estratégias de combate e estudos futuros .Objetivo:Definir o perfil epidemiológico da neoplasia maligna da pele  no Brasil.Métodos: Trata-se de perfil epidemiológico horizontal retrospectivo que utiliza dados do datasus com o recorte dos anos de 2018 a 2023.Resultado: Foram registrados 44.735  internações  no período de 2018-2023. Com isso, foram observados mais casos na faixa etária dos 60   anos ou mais , em que foram notificados 25.548 internações.Discussão: O ano de 2020 teve 6.387 internações  que representa uma queda no número de notificações em relação a 2019 com 7.532 internações. Ao comparar o período pandêmico com o pós pandêmico foi observado o crescimento no número de notificações . Possivelmente houve uma subnotificação em razão da superlotação de hospitais durante o período pandêmico. Conclusão: Nesse sentido , fica evidente que a superlotação dos hospitais no período pandêmico foi um fator determinante para a diminuição dos casos notificados bem como as internações pelo câncer de pele . O presente estudo tem por finalidade inspirar futuras políticas públicas no combate a neoplasia maligna da pele.
Mecanismos fisiopatológicos do edema cerebral no acidente vascular encefálico isquêmico: uma revisão com relato de caso
Authors: Pinheiro Jármison Luciano , Barbosa Lara Beatriz Amorim , Alencar Wellington Douglas Santos de , Souza João Lucas de Almeida Silva , Matos João Paulo Abdallah , Silva Vitória Teixeira da , Nunes Larissa Graziela da Silva , Santos Tainá Gallina Dos
Published: December 2024

O acidente vascular encefálico (AVE) isquêmico é uma emergência hipertensiva causada pela interrupção do fluxo sanguíneo em um território vascular cerebral. Essa condição é responsável pela maioria dos casos de AVE no Brasil e na Bolívia e é a principal causa de deterioração cognitiva e mortalidade na população adulta afetada por eventos cerebrovasculares agudos. Este estudo visou analisar a evolução clínica de um paciente de 74 anos, do sexo masculino, com comorbidades, que chegou ao pronto-socorro de um hospital terciário com quadro clínico e exames de imagem compatíveis com AVE isquêmico, na qual, evoluiu com edema cerebral e hipertensão intracraniana, além de realizar uma revisão teórica para compreender os mecanismos fisiopatológicos relacionados ao edema cerebral em lesão isquêmica. Trata-se de uma revisão retrospectiva do prontuário médico, análise dos exames complementares e revisão bibliográfica, com abordagem qualitativa de caráter descritivo, realizada nos meses de setembro e outubro de 2024. Ao finalizar essa investigação, concluímos que o edema cerebral decorre da ruptura da barreira hematoencefálica, bem como de mecanismos citotóxicos e vasogênicos, configurando-se como um fator crucial na mortalidade associada ao AVE isquêmico. Portanto, intervenções terapêuticas oportunas e assertivas tornam-se essenciais para maximizar os resultados clínicos.
Análise de prescrições de medicamentos psicotrópicos no município de Cataguases - MG
Authors: Andrade Lucas Duraes Filho de , Martins Tainá Soares , Rodrigues Ana Paula França , Mardegan Horrana Acácio , Ferreira Gleison Gonçalves , Ataíde Allana Martins dos Santos , Mendonça Maria Helena Holanda de , Carvalho Isabela de Oliveira
Published: December 2024

Com o crescimento de prescrições de psicofármacos nas últimas décadas, verificar o consumo de medicamentos psicotrópicos demanda séria reflexão. Assim, realizou-se um estudo observacional descritivo transversal do tipo retrospectivo para determinar os padrões de comercialização de medicamentos sujeitos a controle especial com a avaliação de 160 prescrições dispensadas em uma drogaria, durante o período de janeiro a julho de 2022, na cidade de Cataguases, MG. Dessa forma, observa-se que a porcentagem de medicamentos psicotrópicos prescritos por médicos difere entre homens e mulheres com 63% correspondendo ao sexo feminino e 37% ao masculino. A análise qualitativa revelou que os 3 medicamentos mais prescritos foram Rivotril, Alprazolam e Bromazepam. Apenas 25% das prescrições apresentavam-se legíveis, além disso, foi possível observar outras irregularidades relacionadas a dose, via de administração, identificação do emitente, identificação do usuário, entre outras. Portanto, considera-se necessário maior fiscalização sanitária em relação às prescrições, já que o consumo de drogas psicotrópicas é responsabilidade da saúde pública, que para promover o uso racional de medicamentos tem de avaliar seus padrões de utilização, além de empregar maiores esforços de prevenção a fim de reduzir o uso indevido e o abuso dessas drogas.
Acessibilidade, dificuldades e avanços dos serviços odontológicos no SUS: uma revisão integrativa
Authors: Silva Rafaela dos Santos , Cabral Ivyson Guilherme da Silva , Santos Antônio Felipe Pessoa , Correia Paola Vitória Magalhães , Souza Camili Guerra Rodrigues de , Ferreira Suédnny Talitha Dias Figueirôa , Gouveia Carlos Monteiro Vasconcelos da Cruz , Silva Ivaldo Eduardo Samico da , Santana Dayanne Larissa Ferreira de , Ramos Adriano Costa
Published: December 2024

Os avanços recentes buscam melhorar a coordenação e o acesso aos cuidados de saúde bucal no SUS, superando dificuldades de infraestrutura e recursos financeiros. Desta forma, havendo progresso na descentralização de recursos e na expansão dos serviços odontológicos gratuitos. O estudo tem como objetivo analisar a trajetória da saúde bucal no SUS, identificando as dificuldades, avanços e acessos aos serviços de saúde, iniciado pela criação das políticas públicas. A pesquisa trata-se de uma revisão integrativa de literatura baseado nos estudos mais atualizados. O pesquisador reunirá informações coletadas, afim de compreender e interpretar os avanços, implicações e o caminho da saúde bucal no SUS. Os resultados obtidos nas publicações estudadas, conclui-se que dentre os principais pontos a respeito da acessibilidade aos serviços, pacientes com deficiência apresenta-se o público mais vulnerável. Quanto as dificuldades relatadas, problemas para marcação de consultas é o mais evidente, representando cerca de 75%. Contudo, os avanços são evidentes, em que 100% dos artigos analisados apresentam pontos distintos de melhoria no SUS. O SUS tem feito avanços importantes na melhoria do acesso aos cuidados odontológicos, como evidenciado pela inclusão de procedimentos de implante dentário e a expansão da atenção primária com o PBS. Essas iniciativas têm contribuído para uma cobertura mais abrangente e para a redução das desigualdades no atendimento.
Gravidez em decorrência de estupro: reflexões legais e bioéticas da Resolução 2.378/2024 do CFM
Authors: Pereira Waddington Araújo , Filho Regino Álvaro de Aragão , Matos Nery Bortolon de , Castro Augusto Castro de , Santos Matheus Eduardo Xavier dos , Chambarelli Milena Utsch , Filho José Maria Marques de Melo , Barbosa Loeste de Arruda
Published: December 2024

Introdução/Justificativa: no Brasil, o aborto é permitido em três situações: anencefalia fetal, risco de morte para a mãe e gravidez decorrente de violência sexual. A clandestinidade e a falta de infraestrutura hospitalar expõem mulheres de baixa renda a riscos elevados. Recentemente, a Resolução nº 2.378 do Conselho Federal de Medicina (CFM) proibiu o uso da técnica de assistolia fetal para interrupções de gestações acima de 22 semanas decorrentes de estupro. Objetivo: este artigo discute os impactos bioéticos da resolução sobre os direitos reprodutivos das mulheres e revisa a literatura relacionada. Metodologia: trata-se de um artigo de opinião fundamentado em uma revisão narrativa da literatura com abordagem qualitativa. Os artigos foram selecionados em bases como SciELO, PubMed e Google Acadêmico, utilizando descritores como “aborto legal”, “estupro” e “bioética”. Resultados: a resolução deveria considerar princípios bioéticos como autonomia e não maleficência, em vez de impor proibições que prejudicam mulheres vítimas de violência. As restrições ao acesso a serviços de aborto legal perpetuam desigualdades e violam direitos reprodutivos, especialmente em contextos vulneráveis. Ignorar as condições socioeconômicas dessas mulheres pode resultar em danos irreparáveis à sua saúde física e mental. Conclusão: é urgente que as decisões sobre aborto legal priorizem a dignidade e os direitos humanos, respeitando a autonomia das mulheres em situações de vulnerabilidade emocional e de saúde.
قضايا حول استعمال الإنترنت في الكويت ودوافعه
Authors: عباس حسن عبدالله , الشواف عبدالرضا حسن
Published: December 2024

تمخض التقدم التقني في شبكة الإنترنت عن ثورة معلوماتية ضخمة حول العالم، تغيرت معها الجوانب الحياتية للناس، فكما أن هذه التقنية أضافت إيجابيات كثيرة على المجتمعات المدنية، فكذلك أوجدت معها كثيراً من المشكلات و القضايا (تقنية، سياسية، أخلاقية، اجتماعية، الخ) بوصفها نتيجة حتمية لهذا الاستخدام. لذا فإن الهدف من هذه الدراسة هو تحديد مجموعة القضايا و المعوقات التي تحول دون الاستفادة من الإنترنت. و تظهر نتائج هذه الدراسة أن الغالبية من مستخدمي الشبكة ممن شملتهم الدراسة كانوا نسبياًّ حديثي العهد بالإنترنت، حيث يتميزون بقدرة متواضعة في مجال التعامل مع الشبكة. كما أنهم لا يعدون الشبكة مصدراً مهماًّ من مصادر المعرفة. كذلك توضح الدراسة أن الإباحية، و الفيروسات، و جرائم الإنترنت هي المعوقات الرئيسة للمستخدمين، وهي التي تصدهم عن التوسع في استخدام الشبكة. وأخيراً، توصي الدراسة بأنه للوصول إلى تحقيق القدرات الكبيرة لهذه الشبكة، من الضروري جداًّ أن تعي مراكز القرار في الدول العربية حيثيات الإنترنت، ووضع هذه القضايا و المعوقات في حسابها.
Navigating Online Narratives on Israel-Hamas War with LLMs
Authors: Maathuis Clara , Kerkhof Iddo
Published: December 2024

In recent years, international conflicts have seen a steady rise, accompanied by the increased use of technological advancements to achieve strategic and military objectives. One such conflict, the ongoing hostilities between Israel and Hamas, exemplifies the complex interplay of political, ideological, social, and technological factors shaping contemporary warfare. While extensive analysis is devoted to analysing strategic, historical, and economic dimensions, still limited attention is directed through research efforts towards the vast troves of information available on global social media platforms such as YouTube. These platforms serve as conduits for a wide range of narratives, opinions, and representations related to this war, by providing valuable insights into public discourse and sentiments. Given the capability of automatically and systematically analysing vast amount of content, this research tackles the above stated knowledge gap by developing a novel explorative LLMs (Large Language Models)-based modelling approach to efficiently extract, categorize, and interpret nuanced discussions surrounding the Israel-Palestinian war. It does that following the Data Science methodological approach and focusing on YouTube data aiming at not only contributing to enriching understanding of contemporary conflicts through social media platforms, but also to reflect on the evolving role that state-of-the-art AI technological developments play in shaping the global security landscape.
Generative AI in Writing Workshops: A Path to AI Literacy
Authors: Gabriel Sonja
Published: December 2024

The widespread use of generative AI tools which can support or even take over several part of the writing process has sparked many discussions about integrity, AI literacy and changes to academic writing processes. This paper explores the impact of generative artificial intelligence (AI) tools on the academic writing pro-cess, drawing on data from a writing workshop and interviews with university students of a university teacher college in Austria. Despite the widespread assumption that generative AI, such as ChatGPT, is widely used by students to support their academic tasks, initial findings suggest a notable gap in participants' experience and understanding of these technologies. This discrepancy highlights the critical need for AI literacy and underscores the importance of familiarity with the potential, challenges and risks associated with generative AI to ensure its ethical and effective use. Through reflective discussions and feedback from work-shop participants, this study shows a differentiated perspective on the role of generative AI in academic writing, illustrating its value in generating ideas and overcoming writer's block, as well as its limitations due to the indispensable nature of human involvement in critical writing tasks.
Comparative Analysis of YOLOv5/v8/v9 for Object Detection, Tracking, and Human Action Recognition in Combat Sports
Authors: Quinn Evan , Corcoran Niall
Published: December 2024

YOLO models are widely used object detectors in computer vision (CV). This study investigates the relative performance of YOLOv5, YOLOv8, and YOLOv9 for object detection, tracking, and human action recognition in combat sports. The models were evaluated using curated datasets encompassing various combat scenarios, athlete movements, and equipment configurations. Pre-processing protocols and augmentation techniques were applied to improve model accuracy and generalizability, including automated orientation correction, image dimension standardisation, contrast enhancement, and methods such as zoom, rotation, shear, and grayscale conversion. The key findings provide insight into the comparative performance of the models across various evaluation metrics, such as precision, recall, and mean average precision. Each model's ability to detect, track, and recognise human actions in dynamic combat sports environments is evaluated. Computational efficiency and real-time performance were assessed as these are important indicators for practical applications in coaching, training, and competitive scoring systems. The findings suggest that YOLOv8 offers the best balance of precision and recall, making it particularly suitable for real-time applications in combat sports analytics. This study contributes to advancing CV technologies in combat sports analytics, with potential implications for improving athletic training methods, facilitating personalised coaching interventions, and enhancing objectivity and consistency in competitive scoring processes in combat sports.
Ethical Generative AI: What Kind of AI Results are Desired by Society?
Authors: Lehmann Marc , Peinl René , Wagener Andreas
Published: December 2024

There are many publications talking about the biases to be found in in generative AI solutions like large language models (LLMs, e.g., Mistral) or text-to-image models (T2IMs, e.g., Stable Diffusion). However, there is merely any publication to be found that questions what kind of behavior is actually desired, not only by a couple of researchers, but by society in general. Most researchers in this area seem to think that there would be a common agreement, but political debate in other areas shows that this is seldom the case, even for a single country. Climate change, for example, is an empirically well-proven scientific fact, 197 countries (including Germany) have declared to do their best to limit global warming to a maximum of 1.5°C in the Paris Agreement, but still renowned German scientists are calling LLMs biased if they state that there is human-made climate change and humanity is doing not enough to stop it. This trend is especially visible in Western individualistic societies that favor personal well-being over common good. In this article, we are exploring different aspects of biases found in LLMs and T2IMs, highlight potential divergence in the perception of ethically desirable outputs and discuss potential solutions with their advantages and drawbacks from the perspective of society. The analysis is carried out in an interdisciplinary manner with the authors coming from as diverse backgrounds as business information systems, political sciences, and law. Our contribution brings new insights to this debate and sheds light on an important aspect of the discussion that is largely ignored up to now.