bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantifying the Impact of Environmental Factors on the Methane Point-Source Emission Algorithm
Authors: Wang Zixuan , Wang Linxin , Li Ding , Yang Lingjing , Cao Lixue , He Qin , Qin Kai
Published: February 2025

Methane (CH4) emissions in coal-energy-rich regions are characterized by hidden emission point sources and highly variable emission rates. While the Matched Filter (MF) method for detecting the CH4 point source using hyperspectral satellite sensors has been validated for high-emission concentrations, the accurate inversion of low-concentration emissions in complex environments remains challenging. In this study, an ‘end-to-end’ experiment—from emission simulations to satellite spectra and inversion results—has been designed to quantify the impact of internal payload parameters and environmental parameters for CH4 emission inversions, and perform real-scenario calculations. The study reveals several key findings: (1) Under ideal conditions, 15% of satellite spectral noise contributes to a 13% bias in CH4 detection inversion, and a spectral resolution of 10–14 nm allows the detection of CH4 emissions with concentrations as low as 350 ppb, above the background level of 1900 ppb. (2) For near-surface aerosols at 2100 nm, an aerosol optical depth (AOD) of 0.1 leads to a low bias of −51.6% with water-soluble aerosols and a strong bias of −69.2% with black carbon aerosols, while dust aerosols induce a medium bias of up to −60.7%. (3) The height of the aerosol layer affects the accuracy of methane inversion, which is up to 7.3% higher under aerosol conditions at 3 km than under aerosol conditions near the ground. (4) When the CH4 emission source and its diffuse plume are located above a high-reflectance (bright) surface, while the background CH4 concentration is associated with a low-reflectance (dark) surface, the significant reflectance contrast between the two surfaces leads to a rapid degradation in inversion accuracy. This contrast makes it impossible to effectively extract CH4 signals when the reflectance difference reaches 0.2. (5) Under harsh conditions, where multiple parameters are present (AOD = 0.2, albedo = 0.2, aerosol layer height (ALH) = 2), the MF method is still able to detect CH4 emissions, but with a significant error of 74.65%. (6) External environmental variables, particularly atmospheric pressure and water vapor content, significantly influence the inversion accuracy of methane (CH4) concentrations. Variations in atmospheric pressure induce deviations in the CH4 concentration distribution, resulting in an average inversion error of −12.06%. Similarly, elevated water vapor levels can lead to a maximum error of −16.2%. These findings highlight the substantial challenges in accurately detecting low-concentration CH4 emissions. The results offer critical insights for refining CH4 detection algorithms and enhancing the precision of satellite-based inversions for low-concentration CH4 point-source emissions.
Spatiotemporal Responses and Vulnerability of Vegetation to Drought in the Ili River Transboundary Basin: A Comprehensive Analysis Based on Copula Theory, SPEI, and NDVI
Authors: Li Yaqian , Yang Jianhua , Wu Jianjun , Zhang Zhenqing , Xia Haobing , Ma Zhuoran , Gao Liang
Published: February 2025

The Ili River Transboundary Basin is an important area within the Belt and Road Initiative, and its ecological security impacts China–Kazakhstan diplomatic relations and the building of the Belt and Road Initiative. Using the copula method, this study quantifies the vulnerability of vegetation to drought in the Ili River Transboundary Basin based on the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The vulnerability of vegetation in the Ili River Transboundary Basin is highest in June, with the proportion of highly vulnerable areas reaching 63.29% under extreme drought conditions. As the drought severity increases, the probability of vegetation loss rises, with vegetation being affected the most in June. From May to June, drought-prone areas are mainly located in Almaty Oblast and East Kazakhstan. From July to September, drought-prone areas are mainly found in the Ili River Valley and southeastern Almaty Oblast. Rainfed croplands are most susceptible to drought, while, for irrigated croplands, higher drought severity enhances the mitigating effect of irrigation measures. Vegetation areas are most affected by drought in semi-arid regions, particularly in summer. These findings offer valuable scientific support for drought management and sustainable development in the region.
MBGPIN: Multi-Branch Generative Prior Integration Network for Super-Resolution Satellite Imagery
Authors: Safarov Furkat , Khojamuratova Ugiloy , Komoliddin Misirov , Bolikulov Furkat , Muksimova Shakhnoza , Cho Young-Im
Published: February 2025

Achieving super-resolution with satellite images is a critical task for enhancing the utility of remote sensing data across various applications, including urban planning, disaster management, and environmental monitoring. Traditional interpolation methods often fail to recover fine details, while deep-learning-based approaches, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly advanced super-resolution performance. Recent studies have explored large-scale models, such as Transformer-based architectures and diffusion models, demonstrating improved texture realism and generalization across diverse datasets. However, these methods frequently have high computational costs and require extensive datasets for training, making real-world deployment challenging. We propose the multi-branch generative prior integration network (MBGPIN) to address these limitations. This novel framework integrates multiscale feature extraction, hybrid attention mechanisms, and generative priors derived from pretrained VQGAN models. The dual-pathway architecture of the MBGPIN includes a feature extraction pathway for spatial features and a generative prior pathway for external guidance, dynamically fused using an adaptive generative prior fusion (AGPF) module. Extensive experiments on benchmark datasets such as UC Merced, NWPU-RESISC45, and RSSCN7 demonstrate that the MBGPIN achieves superior performance compared to state-of-the-art methods, including large-scale super-resolution models. The MBGPIN delivers a higher peak signal-to-noise ratio (PSNR) and higher structural similarity index measure (SSIM) scores while preserving high-frequency details and complex textures. The model also achieves significant computational efficiency, with reduced floating point operations (FLOPs) and faster inference times, making it scalable for real-world applications.
DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images
Authors: Li Yankui , Zhu Wu , Wu Jing , Zhang Ruixuan , Xu Xueyong , Zhou Ye
Published: February 2025

Deep learning-based semantic segmentation algorithms have proven effective in landslide detection. For the past decade, convolutional neural networks (CNNs) have been the prevailing approach for semantic segmentation. Nevertheless, the intrinsic limitations of convolutional operations hinder the acquisition of global contextual information. Recently, Transformers have garnered attention for their exceptional global modeling capabilities. This study proposes a dual-branch semantic aggregation network (DBSANet) by integrating ResNet and a Swin Transformer. A Feature Fusion Module (FFM) is designed to effectively integrate semantic information extracted from the ResNet and Swin Transformer branches. Considering the significant semantic gap between the encoder and decoder, a Spatial Gate Attention Module (SGAM) is used to suppress the noise from the decoder feature maps during decoding and guides the encoder feature maps based on its output, thereby reducing the semantic gap during the fusion of low-level and high-level semantic information. The DBSANet model demonstrated superior performance compared to existing models such as UNet, Deeplabv3+, ResUNet, SwinUNet, TransUNet, TransFuse, and UNetFormer on the Bijie and Luding datasets, achieving IoU values of 77.12% and 75.23%, respectively, with average improvements of 4.91% and 2.96%. This study introduces a novel perspective for landslide detection based on remote sensing images, focusing on how to effectively integrate the strengths of CNNs and Transformers for their application in landslide detection. Furthermore, it offers technical support for the application of hybrid models in landslide detection.
Weakly Supervised Semantic Segmentation of Remote Sensing Images Using Siamese Affinity Network
Authors: Chen Zheng , Lian Yuheng , Bai Jing , Zhang Jingsen , Xiao Zhu , Hou Biao
Published: February 2025

In recent years, weakly supervised semantic segmentation (WSSS) has garnered significant attention in remote sensing image analysis due to its low annotation cost. To address the issues of inaccurate and incomplete seed areas and unreliable pseudo masks in WSSS, we propose a novel WSSS method for remote sensing images based on the Siamese Affinity Network (SAN) and the Segment Anything Model (SAM). First, we design a seed enhancement module for semantic affinity, which strengthens contextual relevance in the feature map by enforcing a unified constraint principle of cross-pixel similarity, thereby capturing semantically similar regions within the image. Second, leveraging the prior notion of cross-view consistency, we employ a Siamese network to regularize the consistency of CAMs from different affine-transformed images, providing additional supervision for weakly supervised learning. Finally, we utilize the SAM segmentation model to generate semantic superpixels, expanding the original CAM seeds to more completely and accurately extract target edges, thereby improving the quality of segmentation pseudo masks. Experimental results on the large-scale remote sensing datasets DRLSD and ISPRS Vaihingen demonstrate that our method achieves segmentation performance close to that of fully supervised semantic segmentation (FSSS) methods on both datasets. Ablation studies further verify the positive optimization effect of each module on segmentation pseudo labels. Our approach exhibits superior localization accuracy and precise visualization effects across different backbone networks, achieving state-of-the-art localization performance.
Automated Detection of Araraucaria angustifolia (Bertol.) Kuntze in Urban Areas Using Google Earth Images and YOLOv7x
Authors: Karasinski Mauro Alessandro , Leite Ramon de Sousa , Guaraná Emmanoella Costa , Figueiredo Evandro Orfanó , Broadbent Eben North , Silva Carlos Alberto , Santos Erica Kerolaine Mendonça dos , Sanquetta Carlos Roberto , Dalla Corte Ana Paula
Published: February 2025

This study addresses the urgent need for effective methods to monitor and conserve Araucaria angustifolia, a critically endangered species of immense ecological and cultural significance in southern Brazil. Using high-resolution satellite images from Google Earth, we apply the YOLOv7x deep learning model to detect this species in two distinct urban contexts in Curitiba, Paraná: isolated trees across the urban landscape and A. angustifolia individuals within forest remnants. Data augmentation techniques, including image rotation, hue and saturation adjustments, and mosaic augmentation, were employed to increase the model’s accuracy and robustness. Through a 5-fold cross-validation, the model achieved a mean Average Precision (AP) of 90.79% and an F1-score of 88.68%. Results show higher detection accuracy in forest remnants, where the homogeneous background of natural landscapes facilitated the identification of trees, compared to urban areas where complex visual elements like building shadows presented challenges. To reduce false positives, especially misclassifications involving palm species, additional annotations were introduced, significantly enhancing performance in urban environments. These findings highlight the potential of integrating remote sensing with deep learning to automate large-scale forest inventories. Furthermore, the study highlights the broader applicability of the YOLOv7x model for urban forestry planning, offering a cost-effective solution for biodiversity monitoring. The integration of predictive data with urban forest maps reveals a spatial correlation between A. angustifolia density and the presence of forest fragments, suggesting that the preservation of these areas is vital for the species’ sustainability. The model’s scalability also opens the door for future applications in ecological monitoring across larger urban areas. As urban environments continue to expand, understanding and conserving key species like A. angustifolia is critical for enhancing biodiversity, resilience, and addressing climate change.
Optimization of Improved YOLOv8 for Precision Tomato Leaf Disease Detection in Sustainable Agriculture
Authors: Shen Yue , Yang Zhaofeng , Khan Zohaib , Liu Hui , Chen Wenhua , Duan Shuyang
Published: February 2025

Increasing demand for sustainable agriculture necessitates precise and efficient crop management to minimize resource wastage and environmental impact. To improve the precision of pesticide application in tomato leaves, a real-time tomato leaf detection method using an improved YOLOv8 algorithm is proposed. The framework was developed by integrating Depthwise Grouped Convolutions and an AdamW optimizer to achieve both computational efficiency and precise detection capabilities. The integration of SE_Block further enhanced feature representation by adaptively recalibrating channel-wise attention, improving detection accuracy and robustness. The algorithm was labeled and trained by using a diverse dataset of 1500 tomato leaf images consisting of four labels (All, Green Tomato, Downy Mildew, and Powdery Mildew), capturing variations in disease types, lighting conditions, and leaf orientations, enabling robust detection performance across real-world scenarios. The incorporation of Depthwise Grouped Convolutions into YOLOv8 reduced the computational complexity, enabling faster inference speed without sacrificing detection accuracy. Additionally, the AdamW optimizer enhanced the model convergence during training, ensuring robustness and stability. Compared with the original algorithm, the improved YOLOv8 achieved a significant performance improvement, with model precision (P%) increasing from 83.5% to 85.7% (2.2% increase), recall (R%) improving from 70.4% to 72.8% (2.4% increase), and mAP@0.5 improving from 75.7% to 79.8% (4.1% increase). mAP@0.5:0.95 also saw an improvement, rising from 44.2% to 51.6% (7.4% increase). Furthermore, the F1 score increased from 76.4% to 78.6% (2.2% increase), demonstrating enhanced overall detection accuracy. The system was deployed on the Spraying Robot LPE-260 to enable real-time, automated pesticide application in controlled environments. The improved detection framework ensures the targeted spraying of diseased tomato leaves, significantly reducing chemical usage and minimizing overspray. This system ensures that pesticide is sprayed exclusively on the diseased areas of tomato leaves, further minimizing chemical usage and overspray. It demonstrates the potential of computationally efficient deep learning techniques to address key challenges in precision agriculture, advancing scalable, sustainable, and resource-efficient crop management solutions.
Scientific Machine Learning for Guided Wave and Surface Acoustic Wave (SAW) Propagation: PgNN, PeNN, PINN, and Neural Operator
Authors: Mehtaj Nafisa , Banerjee Sourav
Published: February 2025

The governing Partial Differential Equation (PDE) for wave propagation or the wave equation involves multi-scale and multi-dimensional oscillatory phenomena. Wave PDE challenges traditional computational methods due to high computational costs with rigid assumptions. The advent of scientific machine learning (SciML) presents a novel paradigm by embedding physical laws within neural network architectures, enabling efficient and accurate solutions. This study explores the evolution of SciML approaches, focusing on PINNs, and evaluates their application in modeling acoustic, elastic, and guided wave propagation. PINN is a gray-box predictive model that offers the strong predictive capabilities of data-driven models but also adheres to the physical laws. Through theoretical analysis and problem-driven examples, the findings demonstrate that PINNs address key limitations of traditional methods, including discretization errors and computational inefficiencies, while offering robust predictive capabilities. Despite current challenges, such as optimization difficulties and scalability constraints, PINNs hold transformative potential for advancing wave propagation modeling. This comprehensive study underscores the transformative potential of PINN, followed by recommendations on why and how it could advance elastic, acoustic, and guided wave propagation modeling and sets the stage for future research in the field of Structural Health Monitoring (SHM)/Nondestructive Evaluation (NDE).
Solar Spectrum Simulation Algorithms Considering AM0G and AM1.5G
Authors: Yang Junjie , Zhang Guoyu , Zhao Bin , Yang Dongpeng , Zhang Ke , Zhang Yu , Zhang Jian , Ren Zhengwei , Sun Jingrui , Wang Lu , Mo Xiaoxu , Ren Taiyang , Ren Dianwu , Peng Zeng , Yang Songzhou , Lv Jiabo
Published: February 2025

LED solar simulators currently face limitations in their spectral simulation capabilities, especially in terms of accurately incorporating AM0G and AM1.5G solar spectra. To this end, this study introduced a framework for an LED solar spectrum simulation algorithm that considers both AM0G and AM1.5G. This study examined the principle of solar spectrum discretization and reconstruction, established a foundation for analyzing the quality of solar spectrum reconstruction, and developed a non-dominated sorting genetic algorithm II (NSGA-II)-assisted long short-term memory (LSTM)-based solar spectrum simulation strategy. This strategy integrates a multi-objective genetic algorithm to generate training datasets and a neural network for solar spectrum simulation. A dataset generation method using the NSGA-II algorithm was implemented, which leveraged the 6500 K standard blackbody spectral curve, the spectral curve offset coefficients, and the spectral distributions of various narrowband LEDs. An LSTM-based neural network for solar spectrum simulation was developed, with the RMSE serving as the evaluation function. The analysis and selection of 29 narrowband LEDs produced 5000 solar spectrum simulation training datasets. The trained LSTM model achieved spectral matching accuracies within ±10.5% and ±9.3% for AM0G and AM1.5G, respectively, meeting the A+ level simulation standard for solar spectrum reconstruction considering AM0G and AM1.5G. These findings provide a theoretical foundation and technical advancements for high-precision solar spectrum reconstruction, which has practical implications for improving the efficiency and accuracy of solar energy systems, as well as supporting further research on solar spectrum utilization, and is expected to influence the development of more efficient solar simulators.
A Multi-Criteria Forest Fire Danger Assessment System on GIS Using Literature-Based Model and Analytical Hierarchy Process Model for Mediterranean Coast of Manavgat, Türkiye
Authors: Ersoy İzzet , Ünsal Emre , Gürsoy Önder
Published: February 2025

Forest fires pose significant environmental and economic risks, particularly in fire-prone regions like the Mediterranean coast of Türkiye. This study presents a comprehensive Forest Fire Danger Assessment System (FoFiDAS), by integrating Geographic Information Systems (GIS), a literature-based model, the Analytical Hierarchy Process (AHP), and machine learning (ML) to improve forest fire danger classification. Both models integrate 13 key parameters identified through the literature. A comparison of these models revealed 53% overlap in fire danger classifications. While the AHP model, based on expert-weighted assessment, provided a more structured and localized classification, the literature-based model relied on broader scientific data but lacked adaptability. Pearson correlation analysis demonstrated a strong correlation between fire danger classifications and historical fire occurrences, with correlation scores of 0.927 (AHP) and 0.939 (literature-based). Further ROC analysis confirmed the predictive performance of both models, yielding AUC values of 0.91 and 0.9121 for the literature-based and AHP models, respectively. Five ML algorithms were used to validate classification performances, with Artificial Neural Network (ANN) achieving the highest accuracy (86.5%). The accuracy of the ANN algorithm exceeded 0.93 for each danger class, and the F1-Score was above 0.85. FoFiDAS offers a reliable tool for fire danger assessment, supporting early intervention and decision making.
Nature-Based Solutions for Flood Mitigation: The Case Study of Kochi
Authors: Aloscious Arun Antony , Artuso Mario , Torabi Moghadam Sara
Published: February 2025

Flood risks are escalating globally due to unplanned urban expansion and the impacts of climate change, posing significant challenges for urban areas and necessitating effective mitigation strategies. Nature-based solutions (NBSs) have emerged as innovative and sustainable approaches for managing flood risks. The International Union for Conservation of Nature (IUCN) defines NBSs as actions that conserve, manage, and restore natural and modified ecosystems to address societal concerns while benefiting both people and the environment. This research focuses on developing NBS strategies for the most flood-prone area within Kochi, a city highly vulnerable to flooding. The study begins with a comprehensive site examination to identify flood sources and causes in Kochi, aiding in selecting flood vulnerability indicators. An analytical framework incorporating flood risk assessment and exposure studies using physical and social indicators, alongside GIS mapping techniques, revealed that approximately half of Kochi is affected. The study identified key vulnerability hotspots, particularly within the Central Business District (CBD), where high population density and inadequate infrastructure exacerbate flood risks. Proposed NBS interventions include restoring natural floodplains, enhancing canal capacities, creating urban forests, and establishing green infrastructure like permeable pavements and rainwater harvesting systems. Key findings emphasize the effectiveness of integrating NBSs with traditional flood management strategies, forming a mixed flood control system. These interventions mitigate flood risks, improve biodiversity, reduce the urban heat island effect, and enhance community well-being. Importantly, the research underscores the role of public participation and community-driven maintenance plans in ensuring the sustainability of NBS interventions. Aligning these strategies with Kochi’s Master Plan 2040 ensures coherence with broader urban planning and climate resilience goals. The research anticipates changes in climate, land use patterns, and urban dynamics to inform NBS suitability in Kochi. Ultimately, the research demonstrates how implementing NBSs can deliver a range of socio-environmental benefits, significantly influencing urban development in vulnerable zones. By advocating for the integration of NBSs into urban infrastructure planning, this study offers a blueprint for resilient and sustainable flood management strategies that are applicable to other coastal cities facing similar challenges.
Investigation into Enhancing Ultrasonic Cleaning Efficiency Through Symmetrical Transducer Configuration
Authors: Wei Lei , Liu Sheng , Dong Fang
Published: February 2025

This paper investigates the symmetrical layout effect in ultrasonic cleaning via acoustic solid coupling simulation, with emphasis on how the symmetrical arrangement of transducers influences sound pressure distribution. Two specific transducer layout methods are examined: uniform arrangement at the bottom and symmetrical arrangement along the sides. The findings indicate that when the tank length is an integer multiple of one-quarter of the acoustic wavelength, the symmetrical side arrangement markedly enhances the sound pressure level within the tank and optimizes the propagation and reflection of acoustic waves. In megasonic cleaning, focusing is achieved through a 7 × 7 transducer array by precisely controlling the phase, and the symmetrical arrangement ensures uniform sound pressure distribution. By integrating 1 MHz megasonic sources from both focused and unfocused configurations, the overall sound pressure distribution and peak sound pressure at the focal point are calculated using multi-physics field coupling simulations. A comparative analysis of the sound fields generated by focused and unfocused sources reveals that the focused source can produce significantly higher sound pressure in specific regions. Leveraging the enhanced cleaning capability of the focused acoustic wave in targeted areas while maintaining broad coverage with the unfocused acoustic wave significantly improves the overall cleaning efficiency. Ultrasonic cleaning finds extensive applications in industries such as electronic component manufacturing, medical device sterilization, and automotive parts cleaning. Its efficiency and environmental friendliness make it highly significant for both daily life and industrial production.
Ultra-High-Energy Cosmic Rays from Active Galactic Nuclei Jets: The Role of Supermassive Black Hole Growth and Accretion States
Authors: Piana Olmo , Pu Hung-Yi
Published: February 2025

Jets emanating from active galactic nuclei (AGNs) represent some of the most formidable particle accelerators in the universe, thereby emerging as viable candidates responsible for the detection of ultra-high-energy cosmic rays (UHECRs). If AGN jets indeed serve as origins of UHECRs, then the diffuse flux of these cosmic rays would be dependent on the power and duty cycle of these jets, which are inherently connected to the nature of black hole accretion flows. In this article, we present our cosmological semi-analytic framework, JET (Jets from Early Times), designed to trace the evolution of jetted AGN populations. This framework serves as a valuable tool for predictive analyses of cosmic ray energy density and, potentially, neutrino energy density. By using JET, we model the formation and evolution of galaxies and supermassive black holes (SMBHs) from z=20 to z=1, incorporating jet formation and feedback mechanisms and distinguishing between various accretion states determined by the SMBH Eddington ratios. The implications of different SMBH growth models on predicting cosmic ray flux are investigated. We provide illustrative examples demonstrating how the associated diffuse UHECR fluxes at the source may vary in relation to the jet production efficiencies and the selected SMBH growth model, linking cosmological models of SMBH growth with astroparticle backgrounds.
The Association of a Lower Risk of Fibromyalgia with Human Papillomavirus Vaccination: A Retrospective Cohort Study from the TriNetX US Collaborative Network
Authors: Shi Lin-Hong , Huo An-Ping , Wang Shiow-Ing , Leong Pui-Ying , Wei James Cheng-Chung
Published: February 2025

Objective: Remarkably similar symptoms have been observed between fibromyalgia patients and those who present adverse events after HPV vaccination. However, no research has been conducted on their association. Methods: Using data from the US collaborative network within the TriNetX network, we identified all the females who had had an HPV vaccination within 1 year before an index date falling between 2016 and 2023. We selected a propensity-score-matched (PSM, 1:1 ratio), non-HPV-vaccinated cohort as the comparator. Both cohorts were followed up from 1 day after the index date until the diagnosis of incidental fibromyalgia or until the patient was lost to the follow-up process or until the end of 2023. Results: After PSM, a total of 421,564 females in the HPV-vaccinated cohort and 421,564 females in the non-HPV-vaccinated cohort were included in the study. Significantly lower risks of developing fibromyalgia after 5 years’ follow-up were consistent in different models after adjusting for different covariates (adjusted hazard ratios [aHRs]: 0.505, 0.665, and 0.601). Also, significantly lower risks of incident fibromyalgia were identified across different follow-up periods, namely, 1 day to 1 year (HR: 0.464; 95% CI, 0.386–0.559), 1 day to 3 years (HR: 0.553; 95% CI, 0.494–0.618), 1 day to 5 years (HR: 0.601; 95% CI, 0.549–0.658), and 1 day to 7 years (HR: 0.636; 95% CI, 0.587–0.690). Conclusions: This study demonstrates that HPV vaccination significantly decreases the risk of developing incident fibromyalgia across different follow-up periods and subgroups. Our study suggests that HPV vaccination may potentially reduce the risk of developing fibromyalgia in female patients, which needs validation through studies of the mechanisms involved.
Transformation of Terraces with Irrigation Systems: Profitability and Water Savings in Potato Crop (Solanum tuberosum L.)
Authors: Poma-Chamana Russell , Flores-Marquez Ricardo , Cordova-Tadeo Joel , Quello Antony , Arapa-Quispe José , Solórzano-Acosta Richard
Published: February 2025

In recent decades, climate change has intensified the challenges in agriculture, increasing the incidence of water and heat stress during critical stages of the crop cycle. It includes the exacerbation of the seasonality of rainfall and temperature. This significantly affects their development and yield. In addition, climate change has reduced irrigation water availability, highlighting the need to evaluate joint strategies to increase water productivity. These strategies include the implementation of irrigation systems, the use of terraces, and the application of deficit irrigation (DI). In this context, the present research aims to evaluate the irrigation water productivity (WPirri) and the economic water productivity (EWP) of the combined use of DI through a pressurized irrigation system in terraces of the southern highlands of Peru for a potato crop. The treatments included L0: traditional surface irrigation with irrigation depth equivalent to 100% ETc, L1: drip irrigation with irrigation depth equivalent to 100% ETc, L2: drip irrigation with DI at 75% ETc, and L3: drip irrigation with DI at 50% ETc. The DI treatments (L2 and L3) were implemented by forming stolons (60 DAS). As a result, L2 allows saving irrigation water of 3930 m3 ha−1 compared to L0 and 1164 m3 ha−1 compared to L1. It means a WPirri of 6.15 ± 0.35 kg m−3 allowing a commercial yield (CY: 27.15 ± 1.47 t ha−1) statistically similar to L1 (WPirri: 5.45 ± 0.34 kg m−3; CY: 30.14 ± 1.83 t ha−1) and higher than the traditional surface irrigation (WPirri: 2.63 ± 0.23 kg m−3; CY: 21.62 ± 1.99 t ha−1). This water saving meant a net income of 3097.04 ± 435.52 USD ha−1 for L2, close to L1 (4421.12 ± 724.24 USD ha−1), and much higher than L0 (1664.50 ± 834.24 USD ha−1). The results suggest that using drip irrigation systems in terraced crops optimizes water savings, maintains yields and profitability, and could promote the modernization of terraces in rural environments.
Particle Size Distribution of Materials and Chemical Element Composition in Mixed Commercial Waste
Authors: Enengel Maximilian Julius , Lasch Tatjana , Kandlbauer Lisa , Viczek Sandra Antonia , Pomberger Roland , Sarc Renato
Published: February 2025

In processing mixed commercial waste (MCW), particle size distribution is as critical as material composition. Detailed knowledge of particle size distribution unlocks the recycling potential of specific material groups and facilitates the efficient conversion of these materials into secondary fuels. Additionally, understanding particle size-dependent element distribution in waste is crucial, particularly given potential legal limits on several heavy metals. While two studies carried out in 2019 have addressed these issues, the inherent variability in MCW composition necessitates further investigation to validate and expand upon these findings. In this study, ten representative samples of MCW were collected and screened with eight screen cuts (200 mm, 100 mm, 80 mm, 60 mm, 40 mm, 20 mm, 10 mm, 5 mm). Six of these fractions (>20 mm) were sorted into 37 material classes, combined again by particle size, and subjected to chemical analyses. These analyses included essential fuel parameters, such as lower heating value and biogenic carbon content, and the concentration of 35 elements across all particle size fractions. A Mann–Whitney U test was conducted to identify correlations in element concentrations between the present study and the study carried out in 2019. Although the results confirm considerable variability in MCW composition, they also reveal trends in element concentrations related to calorific value.
White Identity, Conservatism, and Resistance to COVID-19 Mitigation Strategies
Authors: Grindal Matthew , Huft Justin , Sarathchandra Dilshani
Published: February 2025

Research suggests that opposition to COVID-19 mitigation strategies and mandates has been notable among young adults, especially those who are conservative. The current political climate, through which young adults have developed their political beliefs, has combined conservative values of limited government with pronounced anti-institutional sentiments, thus predisposing today’s younger conservatives to greater skepticism. Young white adults are also experiencing a growing salience in their own racial identities—an experience different from prior generations. No known research has connected these observations by examining the interplay between white identity and conservatism and their impact on support for and adherence to COVID-19 mitigation strategies. Drawing on survey data from a national U.S. sample of white young adults, we examine how two white identity constructs (racial identity and racial identity verification) influence how conservatism impacts resistance to six COVID-19 mitigation outcomes (i.e., vaccination status, masking behavior, support for vaccinations, support for masking, support for vaccination mandates, and support for masking mandates). White racial identity indirectly decreased support for and adherence to all COVID-19 mitigation outcomes by enhancing levels of conservatism. In addition, racial identity verification enhanced the negative effects of conservatism with most of the COVID-19 mitigation outcomes. We discuss these findings within the context of the literature on COVID-19 attitudes and racial identity.
Online Supplement to “High Early Mortality in Patients With Staphylococcus aureus Bloodstream Infection: A Call for Action”
Authors: Mylotte Joseph M.
Published: February 2025

This supplementary material has been provided by the authors to give readers additional information about their work.
Alterações fonoaudiológicas relacionadas à microcefalia
Authors: Pascon Caroline , Silva de Almeida Roseane , Batista de Souza Dalva Maria , Lira Silva Cindy Rafaella , Santos da Silva Willian , Cavalheiro Maria Gabriela , Maximino Luciana Paula , De Castro Corrêa Camila
Published: February 2025

Introdução. Na microcefalia ocorre redução na circunferência occipitofrontal da cabeça, sendo causada por diversos fatores, dentre eles estão relacionados a substância químicas e infecciosas, bem como irradiação, bactérias e vírus. A Fonoaudiologia entra como parte atuante na orientação, estimulação e acompanhamento nas reabilitações nos processos advindos da microcefalia. Objetivo. Descrever as possíveis alterações fonoaudiológicas em indivíduos com microcefalia, por meio de uma revisão bibliográfica. Método. Revisão bibliográfica referente à atuação fonoaudiológica em pacientes com microcefalia no que se refere ao período patológico desde a fase subclínica (diagnóstico, tratamento precoces e limitação do dano) até a fase estacionária do processo para diminuir as sequelas. Resultados. As alterações fonoaudiológicas identificadas foram variáveis e heterogêneas, não sendo possível estabelecer um padrão de sintomas, porém destaca-se que na maioria dos quadros há probabilidade de ao menos uma característica fonoaudiológica estar presente, desta forma. Conclusão. O estudo contribui para a compreensão das alterações de linguagem, auditivas e miofuncionais orofaciais em crianças com microcefalia, evidenciando déficits que são de competência do fonoaudiólogo avaliar, diagnosticar e intervir.
The Use and Absence of the Augment in the Forms ἔθηκ(ε)(ν) and θῆκ(ε)(ν) in the Odyssey
Authors: Decker Filip de
Published: February 2025

In this article, I discuss the use and absence of the augment in the 3rd person singular forms ἔθηκ(ε)(ν) and θῆκ(ε)(ν) in the Odyssey. First, I investigate which forms can be considered metrically secure. In order to do so, I use metrical bridges and caesurae. Then, I discuss the forms in which the metre is not entirely conclusive and try to determine by internal comparison whether the absence or presence of the augment can be accepted. Once I have established a corpus of usable forms, I analyse them and check if they confirm the previous syntactic and semantic observations that have been made for the use and absence of the augment (the clitic rule by Drewitt & Beck, the reduction rule by Kiparsky and the distinctions between speech versus narrative, foreground versus background and remote versus recent past).
Tragique et optimisme en politique : l’exemple du Dyscolos de Ménandre
Authors: Blanchard Alain
Published: February 2025

Le Dyscolos de Ménandre offre un excellent exemple du paradoxe que constitue la liaison du tragique et de l’optimisme. Le paradoxe se résout finalement par une dissolution des deux termes, leçon de politique que la pièce peut très bien apporter pour peu qu’on la replace dans son contexte historique.
Fables in Epigrams of the Greek Anthology
Authors: Christodoulou Maria
Published: February 2025

This article catalogues and interprets the dimensions of Greek epigrammatists’ relationship with the “ Aesopic” fable. It continues the inquiry of Rodriguez Adrados-van Dijk (1999-2003) and sketches the impact of “ Aesopic” animal fables on epigrams of the Hellenistic and first Imperial ages, as well as the reasons for the Hellenistic and Imperial-age epigrammatists’ preference for fables. The rhetorical reworking of fables by epigrammatists is observed and described (imitation, recreation, variation, amplification), especially in epigrams of the epideictic subgenre. In conclusion, morphological elements common to fables and certain epigrams are noted.
La datation de la réforme calendaire d’Asie
Authors: Duvivier Samuel
Published: February 2025

La réforme calendaire de la province d’Asie initiée par Paullus Fabius Maximus est traditionnellement datée en janvier 9 av. J.-C. Deux nouvelles datations ont récemment été proposées : 8 et 7 av. J.-C. (dans le second cas, la réforme serait entrée en vigueur deux ans après sa promulgation). Les éléments permettant de dater la réforme sont ici minutieusement analysés. Le premier point aborde la question du proconsulat de Maximus et les éléments institutionnels qui pourraient avoir une influence sur sa datation. La seconde section traite des années bissextiles depuis l’instauration du calendrier julien (45 av. J.-C.) jusqu’à son amendement par Auguste (8 av. J.-C.) ; les modèles explicatifs existants sont comparés afin de déterminer le meilleur d’entre eux. La troisième partie conclut à une datation de la réforme en janvier 8 av. J.-C. En annexe est proposée une nouvelle édition des documents composant le dossier épigraphique de la réforme, accompagnée d’une traduction française.
Generic Interplay and Callimachean Theory in Claudian’s De raptu Proserpinae II, 277-306
Authors: Πάππας Βασίλειος ν.
Published: February 2025

In his unfinished mythological epic poem entitled De raptu Proserpinae Claudian narrates the well-known myth of the rape of Proserpina by Pluto. In II, 277-306, Pluto tries to convince the young girl to follow him to the Underworld and become his queen. Scholars have noted that in Pluto’s speech Claudian conforms to a characteristic feature of the poets of Late Antiquity, i. e. generic interplay. More precisely, they have examined the mixture of epic, elegy and epithalamium that takes place in this passage. In this paper, I discuss the generic interplay between the aforementioned genres in the speech, and I mention the presence of another two, i. e. pastoral and hymnic poetry. Furthermore, I study the metapoetical dimension of the passage and especially the impact of Callimachean poetical theory on it, a subject that has previously been ignored.
Suporte básico de vidas nas escolas: um relato de experiência
Authors: Pereira Taynara Caroline , Corbelli Beatriz de Souza , Oliveira Paloma Abrantes de , Antonieto Mariane Santos
Published: February 2025

Introdução: O Suporte Básico de Vida (SBV) é o primeiro atendimento à vítima de parada cardiorrespiratória e pode ser realizado por qualquer pessoa, sendo fundamental o conhecimento sobre o assunto pela população. Objetivo: Disseminar o conhecimento acerca do SBV para a população leiga. Metodologia: Trata-se de um relato de experiência de um projeto sobre SBV oferecido por acadêmicos de medicina para alunos do ensino médio do município de Ubá-MG. Resultados: o projeto proporcionou aos alunos o conhecimento prático e teórico sobre o atendimento à vítima de parada cardiorrespiratória e de obstrução de vias aéreas através de aulas expositivas e treinamentos práticos. Conclusão: a importância do SBV demonstra a necessidade de mais projetos que levem o conhecimento sobre esse atendimento à população leiga.
Análise comparativa entre apendicectomia via aberta e videolaparoscópica no Brasil na última década
Authors: Oliveira Carlos Eduardo de Melo , Sousa Iara Bernardino de , Brasilino Carla Emanoela de Melo , Cordeiro Ellen Maria de Sousa , Tavares Aretha Holanda , Oliveira Maria Clara Lopes Cordeiro de , Monteiro Leonardo Barroso , Lobo Anna Beatriz Nobre , Ximenes Beatriz Paiva de Carvalho , Souza Thayana Cruz de
Published: February 2025

A apendicite é uma inflamação do apêndice vermiforme que geralmente requer intervenção cirúrgica, seja por via aberta ou videolaparoscópica. O estudo tem como objetivo comparar as características e desfechos da apendicectomia realizada por via aberta e videolaparoscópica. A pesquisa possui caráter descritivo, quantitativo e retrospectivo, para a qual utilizou-se dados secundários, coletados em janeiro de 2025. Utilizou-se cálculos de frequência absoluta e relativa, média, razão de prevalência e teste qui-quadrado de Pearson. No decênio, foram realizadas 1.140.561 apendicectomias no Brasil, sendo 93,4% por via aberta e 6,6% por via laparoscópica.   O total de óbitos foi de 2.722 na via aberta e 57 na videolaparoscopia. Ademais, o TPMH e o valor médio da AIH foram, respectivamente, 2,9 dias e R$ 677,75 para a videolaparoscopia, e 3,3 dias e R$ 616,20 para a via aberta. Observou-se menor tempo de internação, menor taxa de mortalidade e maior valor médio AIH entre os procedimentos realizados por laparoscopia. Apesar disso, a cirurgia aberta ainda é mais comum no Brasil. A comparação entre as técnicas mostrou associação significativa entre a apendicectomia aberta e maior prevalência de óbitos.
Trabalho de parto em pacientes com cesariana prévia: uma revisão integrativa
Authors: França Gabryela de Sousa , Sousa Alan Lopes de , Sousa Angela Gabrielle Santos , Sousa Anna Márcia Leal de , Sousa Jocsam Henrique Gomes de , Carvalho Savina Santos , Júnior José Lopes Pereira , Santos Thainá Pinto dos
Published: February 2025

Introdução: O parto cesáreo é uma das formas mais comuns de nascimento, representando cerca de 55% dos nascimentos. Todavia, pouco se sabe sobre as avaliações necessárias para apontar o melhor método de parto. Portanto, notou-se a necessidade de avaliar essa prática em mulheres com cesariana prévia. Objetivo: Analisar o risco do trabalho de parto em pacientes que tiveram cesariana prévia. Metodologia: Trata-se de uma revisão integrativa baseada na busca sistemática na base PubMed mediante os descritores "Cesárea”, “Nascimento Vaginal Após Cesárea”, “Trabalho de Parto” e “Parto Obstétrico” - buscados separadamente e combinados pelo operador booleano AND. Resultados: Observou-se que não há diferenças relevantes na transfusão, infecção e lesão operatória quando comparados os partos vaginal e cesárea. Ademais, a maioria da morbilidade materna e neonatal estava associada ao fracasso do ensaio de parto, seguido de cesariana de repetição. Ademais, mulheres que tiveram uma cesariana anterior e que foram submetidas a trabalho de parto tiveram uma taxa de tentativa consideravelmente bem sucedida, sobretudo, as pacientes que tiveram mais de um parto vaginal anterior. Conclusão: Como a maior morbidade está atrelada a um trabalho de parto malogrado após uma cesárea anterior, uma melhor seleção de mulheres susceptíveis de serem submetidas a um parto vaginal bem sucedido diminuiria esses riscos. Portanto, as investigações futuras devem concentrar-se nos fatores que preveem um parto vaginal bem sucedido após cesárea prévia, para que se avalie a melhor via de parto.
Aumento de infecções respiratórias em pacientes pediátricos com leucemia
Authors: Lima Natália de Oliveira , Assis Júlia Leandra Nunes de , Lima Leonardo da Costa , Silva Gabriela Alves da , Rocha Vitória Luísa Silveira
Published: February 2025

Introdução: O aumento das infecções respiratórias agudas (IRAs) em pacientes pediátricos com leucemia é uma preocupação significativa tanto pela facilidade de contágio quanto pelo aumento da gravidade e da duração de tais quadros infecciosos em uma população fragilizada imunologicamente. O câncer mais recorrente nessa faixa etária é a leucemia linfocítica aguda, um câncer hematológico, o qual cursa com abordagens terapêuticas ligadas diretamente com imunossupressão, como a quimioterapia e o transplante de células tronco hematopoiéticas. Por conseguinte, pacientes pediátricos com leucemia são mais vulneráveis a infecções virais, bacterianas e fúngicas. A identificação precoce e o tratamento adequado são cruciais para reduzir a mortalidade associada. Objetivo: Analisar as causas do aumento de infecções respiratórias na população de pacientes pediátricos portadores de leucemia. Metodologia: Trata-se de uma revisão de literatura, na qual foram selecionados artigos das seguintes bases de dados: Pubmed e Scielo. Os critérios de inclusão foram artigos completos e gratuitos, publicados entre 2003 e 2024, sem restrições de idioma, específicos da faixa etária pediátrica. Resultado e Discussão: A recorrência de infecções respiratórias na faixa etária infantil já é um tópico conhecido por pediatras, porque trata-se de microrganismos com elevada capacidade de contágio e de proliferação. Para as crianças portadoras de leucemias, o quadro não só é diferente, como também é agravado. A deficiência medular de hematopoese normotípica gera a ocupação blástica maligna da linhagem mieloide ou linfoide. Por consequência, acarreta uma queda significativa da defesa humoral, celular e inata. Desse modo, nas IRAs há um elevado risco de complicações, porque nem sempre as infecções são precedidas por sintomatologia respiratória alta, comprometendo mais as vias respiratórias baixas. Portanto, necessita-se de monitoramento rigoroso e de intervenções preventivas, como vacinação, controle de infecções intra hospitalares e medidas de isolamento de pessoas e de pacientes suspeitos ou com diagnóstico fechado de infecções. Considerações Finais: Por fim, o estudo mostrou que crianças com leucemia apresentam maior vulnerabilidade a infecções devido à quimioterapia e à neutropenia, o que compromete suas defesas naturais. Infecções virais, como as causadas pelo vírus sincicial respiratório (VSR), são as mais prevalentes e perigosas, aumentando a morbidade e a mortalidade. Assim, os tratamentos profiláticos, como o uso de anticorpos monoclonais e vacinas, são fundamentais para reduzir o risco de complicações respiratórias graves​.