bluuebunny/crossref_metadata_2025 huggingface dataset:
“Get the Joke or Get the Jew”: Satire and the Performance of Antisemitism from the Middle Ages to the Twenty-First Century
Authors: Offenberg Sara
Published: December 2024

The persistence of anti-Jewish and antisemitic stereotypes throughout history, from medieval times to the present, reveals the enduring power of visual and cultural narratives in shaping public perceptions of Jews. This paper examines how Yvan Attal’s film Ils sont partout effectively satirizes these stereotypes, exposing their absurdity and the dangers of such ingrained prejudices. By connecting modern satire to historical instances of antisemitism, this study emphasizes the necessity of challenging and critically analyzing these harmful depictions. While the forms of anti-Jewish and antisemitism evolve over time, the underlying biases remain disturbingly consistent across cultures and eras.
Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea
Authors: Wang Zhaolong , Zhang Xiaokuan , Feng Weike , Zong Binfeng , Wang Tong , Qi Cheng , Chen Xixi
Published: December 2024

An intelligent approach is proposed and investigated in this paper for the detection of ultra-low-altitude sea-skimming moving targets for airborne pulse Doppler radar. Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their distinguishable image features and deep learning (DL) techniques. First, the image features of the target, multipath, and sea clutter in the real-measured range-Doppler (RD) map are analyzed, based on which the target and multipath are defined together as the generalized target. Then, based on the composite electromagnetic scattering mechanism of the target and the ocean surface, a scattering-based echo generation model is established and validated to generate sufficient data for DL network training. Finally, the RD features of the generalized target are learned by training the DL-based target detector, such as you-only-look-once version 7 (YOLOv7) and Faster R-CNN. The detection results show the high performance of the proposed method on both simulated and real-measured data without suppressing interferences (e.g., clutter, jamming, and noise). In particular, even if the target is submerged in clutter, the target can still be detected by the proposed method based on the multipath feature.
Deep Learning Method for Wetland Segmentation in Unmanned Aerial Vehicle Multispectral Imagery
Authors: Nuradili Pakezhamu , Zhou Ji , Zhou Guiyun , Melgani Farid
Published: December 2024

This study highlights the importance of unmanned aerial vehicle (UAV) multispectral (MS) imagery for the accurate delineation and analysis of wetland ecosystems, which is crucial for their conservation and management. We present an enhanced semantic segmentation algorithm designed for UAV MS imagery, which incorporates thermal infrared (TIR) data to improve segmentation outcomes. Our approach, involving meticulous image preprocessing, customized network architecture, and iterative training procedures, aims to refine wetland boundary delineation. The algorithm demonstrates strong segmentation results, including a mean pixel accuracy (MPA) of 90.35% and a mean intersection over union (MIOU) of 73.87% across different classes, with a pixel accuracy (PA) of 95.42% and an intersection over union (IOU) of 90.46% for the wetland class. The integration of TIR data with MS imagery not only enriches the feature set for segmentation but also, to some extent, helps address data imbalance issues, contributing to a more refined ecological analysis. This approach, along with the development of a comprehensive dataset that reflects the diversity of wetland environments and advances the utility of remote sensing technologies in ecological monitoring. This research lays the groundwork for more detailed and informative UAV-based evaluations of wetland health and integrity.
Significant Wave Height Retrieval in Tropical Cyclone Conditions Using CYGNSS Data
Authors: Han Xiangyang , Wang Xianwei , He Zhi , Wu Jinhua
Published: December 2024

The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions.
The Impact of Autumn Snowfall on Vegetation Indices and Autumn Phenology Estimation
Authors: Tang Yao , Chen Jin , Xu Jingyi , Xu Jiahui , Ni Jingwen , Zheng Zhaojun , Yu Bailang , Wu Jianping , Huang Yan
Published: December 2024

Monitoring autumn vegetation dynamics in alpine regions is crucial for managing local livestock, understanding regional productivity, and assessing the responses of alpine regions to climate change. However, remote sensing-based vegetation monitoring is significantly affected by snowfall. The impact of autumn snowfall, particularly when vegetation has not fully entered dormancy, has been largely overlooked. To demonstrate the uncertainties caused by autumn snowfall in remote sensing-based vegetation monitoring, we analyzed 16 short-term snowfall events in the Qinghai–Tibet Plateau. We employed a synthetic difference-in-differences estimation framework and conducted simulated experiments to isolate the impact of snowfall from other factors, revealing its effects on vegetation indices (VIs) and autumn phenology estimation. Our findings indicate that autumn snowfall notably affects commonly used VIs and their associated phenology estimates. Modified VIs (i.e., Normalized Difference Infrared Index (NDII), Phenology Index (PI), Normalized Difference Phenology Index (NDPI), and Normalized Difference Greenness Index (NDGI)) revealed greater resilience to snowfall compared to conventional VIs (i.e., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) in phenology estimation. Areas with remaining green vegetation in autumn showed more pronounced numerical changes in VIs due to snowfall. Furthermore, the impact of autumn snowfall closely correlated with underlying vegetation types. Forested areas experienced less impact from snowfall compared to grass- and shrub-dominated regions. Earlier snowfall onset and increased snowfall frequency further exacerbated deviations in estimated phenology caused by snowfall. This study highlights the significant impact of autumn snowfall on remote sensing-based vegetation monitoring and provides a scientific basis for accurate vegetation studies in high-altitude regions.
Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy
Authors: Delogu Gabriele , Perretta Miriam , Caputi Eros , Patriarca Alessio , Funsten Cassandra Carroll , Recanatesi Fabio , Ripa Maria Nicolina , Boccia Lorenzo
Published: December 2024

Hyperspectral imagery and advanced classification techniques can significantly enhance remote sensing’s role in forest monitoring. Thanks to recent missions, such as the Italian Space Agency’s PRISMA (PRecursore IperSpettrale della Missione Applicativa—Hyperspectral PRecursor of the Application Mission), hyperspectral data in narrow bands spanning visible/near infrared to shortwave infrared are now available. In this study, hyperspectral data from PRISMA were used with the aim of testing the applicability of PRISMA with different band sizes to classify tree species in highly biodiverse forest environments. The Serre Regional Park in southern Italy was used as a case study. The classification focused on forest category classes based on the predominant tree species in sample plots. Ground truth data were collected using a global positioning system together with a smartphone application to test its contribution to facilitating field data collection. The final result, measured on a test dataset, showed an F1 greater than 0.75 for four classes: fir (0.81), pine (0.77), beech (0.90), and holm oak (0.82). Beech forests showed the highest accuracy (0.92), while chestnut forests (0.68) and a mixed class of hygrophilous species (0.69) showed lower accuracy. These results demonstrate the potential of hyperspectral spaceborne data for identifying trends in spectral signatures for forest tree classification.
A Full-Scale Shadow Detection Network Based on Multiple Attention Mechanisms for Remote-Sensing Images
Authors: Zhang Lei , Zhang Qing , Wu Yu , Zhang Yanfeng , Xiang Shan , Xie Donghai , Wang Zeyu
Published: December 2024

Shadows degrade image quality and complicate interpretation, underscoring the importance of accurate shadow detection for many image analysis tasks. However, due to the complex backgrounds and variable shadow characteristics of remote sensing images (RSIs), existing methods often struggle with accurately detecting shadows of various scales and misclassifying dark, non-shaded areas as shadows. To address these issues, we proposed a comprehensive shadow detection network called MAMNet. Firstly, we proposed a multi-scale spatial channel attention fusion module, which extracted multi-scale features incorporating both spatial and channel information, allowing the model to flexibly adapt to shadows of different scales. Secondly, to address the issue of false detection in non-shadow areas, we introduced a criss-cross attention module, enabling non-shadow pixels to be compared with other shadow and non-shadow pixels in the same row and column, learning similar features of pixels in the same category, which improved the classification accuracy of non-shadow pixels. Finally, to address the issue of important information from the other two modules being lost due to continuous upsampling during the decoding phase, we proposed an auxiliary branch module to assist the main branch in decision-making, ensuring that the final output retained the key information from all stages. The experimental results demonstrated that the model outperformed the current state-of-the-art RSI shadow detection method on the aerial imagery dataset for shadow detection (AISD). The model achieved an overall accuracy (OA) of 97.50%, an F1 score of 94.07%, an intersection over union (IOU) of 88.87%, a precision of 95.06%, and a BER of 4.05%, respectively. Additionally, visualization results indicated that our model could effectively detect shadows of various scales while avoiding false detection in non-shadow areas. Therefore, this model offers an efficient solution for shadow detection in aerial imagery.
Deep-Transfer-Learning Strategies for Crop Yield Prediction Using Climate Records and Satellite Image Time-Series Data
Authors: Joshi Abhasha , Pradhan Biswajeet , Chakraborty Subrata , Varatharajoo Renuganth , Gite Shilpa , Alamri Abdullah
Published: December 2024

The timely and reliable prediction of crop yields on a larger scale is crucial for ensuring a stable food supply and food security. In the last few years, many studies have demonstrated that deep learning can offer reliable solutions for crop yield prediction. However, a key challenge in applying deep-learning models to crop yield prediction is their reliance on extensive training data, which are often lacking in many parts of the world. To address this challenge, this study introduces TrAdaBoost.R2, along with fine-tuning and domain-adversarial neural network deep-transfer-learning strategies, for predicting the winter wheat yield across diverse climatic zones in the USA. All methods used the bidirectional LSTM (BiLSTM) architecture to leverage its sequential feature extraction capabilities. The proposed transfer-learning approaches outperformed the baseline deep-learning model, with mean absolute error reductions ranging from 9% to 28%, demonstrating the effectiveness of these methods. Furthermore, the results demonstrate that the semi-supervised transfer-learning approach using the two-stage version of TrAdaBoost.R2 and fine-tuning achieved a superior performance compared to the domain-adversarial neural network and standard TrAdaBoost.R2. Additionally, the study offers insights for improving the accuracy and generalizability of crop yield prediction models in diverse agricultural landscapes across different regions.
Evaluation of a Grid-Connected Photovoltaic System at the University of Brasília Based on Brazilian Standard for Performance Monitoring and Analysis
Authors: Fernandes Paulo , Reis Alex , Velasco Loana N. , Francisco Tânia M. , Resende Ênio C. , Freitas Luiz C. G.
Published: December 2024

This work presents the results of research aimed at evaluating the performance of the photovoltaic system connected to the electrical grid at the University of Brasília (UnB), Brazil. Following the guidelines established by the Brazilian Standard for Performance Monitoring and Analysis of Grid-connected Photovoltaic Systems, it was possible to evaluate the system’s performance by determining the Performance Ratio (PR) indicator. The operating temperatures were estimated using measured values of the ambient temperature and solar irradiation. These data were collected by a nearby solarimetric station. Next, the theoretical energy injected into the electrical grid was determined based on calculations of the Direct Current (DC) power at the inverter input and the Alternating Current (AC) power at the inverter output. To this end, the coefficients of the inverter efficiency curve were considered as well as a loss scenario, as recommended. With these results, as well as the information about the total photovoltaics (PV) system AC production obtained from the inverter supervisory system, it was possible to determine the average annual PR achieved and compare the theoretical and practical results obtained. The main contribution of this paper is the performance evaluation of a 125 kWp grid-connected photovoltaic system at the University of Brasília (UnB), assessed using Brazilian Standards for performance monitoring and analysis. The system, installed on the rooftop of the UED building, consists of 298 Canadian Solar HiKu CS3W-420P modules with a 15-degree north pitch angle facing geographic north. It interfaces with the grid through two three-phase inverters, model CSI-75K-T400 (74.76 kWp) and a CSI-50KTL-GI (50.4 kWp). The results showed that the system with a 50kW inverter had an average PR of 78%, while the system with a 75 kW inverter showed a PR variation from 56% to 93%. The information obtained in this work will be used to develop computational tools capable of monitoring and evaluating, in real time, the performance of photovoltaic systems and ensuring that the expected financial return is achieved through the use of preventive and corrective maintenance actions in a timely manner.
Saying Is Believing: How Do Digital Disclosure Anomalies Affect Corporate Innovation Efficiency
Authors: Zhang Zhirui , Fu Yi , Liang Xiaoyin , Jiang Liang
Published: December 2024

Digital transformation has become a new driving force for corporate development, widely embraced by managers. However, in the actual digital transformation process, some companies’ performances are characterized by “more talk, less action”. To measure the phenomenon, this paper innovatively constructs the Digital Disclosure Anomaly (DDA) indicator using Chinese A-share listed companies’ data from 2013 to 2021 and examines the impact of DDA on corporate innovation efficiency by Saying is Believing theory. We find that DDAs show a long-term negative impact on corporate innovation efficiency. Our mechanism analysis suggests that DDAs reduce innovation efficiency through resource misallocation, which is driven by two mechanisms: the manager squandering effect and the inefficient investment effect. Our results suggest that DDAs are harmful for corporate innovation efficiency by the Saying is Believing (SIB) effect and corporate DDA behavior is detrimental to sustainability in digital economy development.
Microalgal Bioeconomy: A Green Economy Approach Towards Achieving Sustainable Development Goals
Authors: Sarker Nilay Kumar , Kaparaju Prasad
Published: December 2024

This article delves into the role of microalgae in advancing a green economy, thereby contributing to the attainment of Sustainable Development Goals (SDGs). Microalgae, as sustainable resources, offer multifaceted benefits across various sectors, including aquaculture, agriculture, food and feed, pharmaceuticals, cosmetics, wastewater treatment, and carbon sequestration. This review highlights the versatility of microalgae in producing biofuels, high-value bioactive compounds, and bioremediation processes. It examines the technical viability and environmental sustainability of microalgae cultivation, emphasizing its low carbon footprint and resource efficiency. This article also explores the integration of microalgae into existing industrial processes, illustrating their potential to mitigate climate change, promote biodiversity, and enhance resource circularity. Challenges such as scalability, cost-effectiveness, and regulatory frameworks are discussed alongside the prospects for technological innovations and policy support to bolster the microalgae industry. By harnessing the potential of microalgae, this article underscores a pathway towards a more sustainable and greener future, aligning with the global agenda for sustainable development.
Innovations in Green Concrete: Combining Metakaolin and Arundo Grass Biochar for Enhanced Sustainability
Authors: Rose Daniel , Shirzad Sharareh
Published: December 2024

Cement production is a major contributor to greenhouse gas (GHG) emissions, driving the need for alternative materials to reduce its environmental footprint and enhance sustainability. This study investigates the use of biochar derived from Arundo grass as a partial replacement for cement in conjunction with metakaolin to enhance the mechanical properties and environmental performance of concrete. Compressive strength analysis and sorptivity analysis were conducted to evaluate the effects of metakaolin on Arundo grass biochar concrete. The findings revealed that incorporating biochar and metakaolin negatively impacted workability. However, a mixture of 5% biochar and 10% metakaolin (by weight of cement) significantly improved early 7-day compressive strength compared to samples containing only 5% biochar and the control mix. Additionally, the sorptivity analysis indicated that this combination maintained comparable absorption rates to the control sample. In terms of sustainability, the partial replacement of cement with 5% biochar and 10% metakaolin reduced CO2 emissions by 75 kg per cubic meter of concrete, showcasing its contribution to lowering the carbon footprint of concrete production. Overall, this study demonstrates the potential of combining biochar and metakaolin to develop more sustainable concrete solutions with enhanced early compressive strength. However, further research is needed to optimize long-term performance and workability for broader adoption in sustainable construction practices.
Analysis of Skidder Fuel Consumption by Work Operations During Timber Extraction in Thinning of Even-Aged Forest on Mountainous Terrain: A Case Study
Authors: Šušnjar Marijan , Pandur Zdravko , Bačić Marin , Halilović Velid , Nevečerel Hrvoje , Lepoglavec Kruno , Kopseak Hrvoje
Published: December 2024

The paper deals with the analysis of the fuel consumption of skidders during timber extraction from thinning of even-aged beech forest on mountain terrain. Fuel consumption research was conducted on the Ecotrac 140V cable skidder over 8 working days at the same worksite during real timber extraction work. The worksite was organized so that the empty skidder traveled uphill, and when loaded, it moved downhill. The skidder was equipped with measuring devices for collecting data from sensors, the motor, and data transfer. The key parameters measured include total fuel consumption (mL) and skidder GPS position, while slopes of skid trails and load volumes were measured directly on terrain. Fuel consumption (L, L/m3) was determined per work cycle and work cycle elements. The highest fuel consumption occurred while driving the unloaded skidder, accounting for 38% of the total. This is primarily because fuel usage during skidder movement is significantly affected by factors such as skidding distance, slope, and skid trail conditions, especially since the unloaded skidder was moving uphill. Guidelines for better and more efficient organization of work and reduction in fuel costs are presented, and the suitability of the skidder and harvesting system are considered based on the results of fuel consumption.
Assessing Renewable Energy Development Potential in Polish Voivodeships: A Comparative Regional Analysis
Authors: Wasiuta Aleksander
Published: December 2024

This work evaluates the renewable energy development potential of Polish voivodeships based on the TOPSIS method and spatial autocorrelation analysis. Data were obtained from the Polish Local Data Bank, covering 22 indicators in the field of economic, social, environmental, and energy related to renewable energy initiatives. The TOPSIS method was applied to construct a synthetic indicator for each voivodeship, facilitating a hierarchical ranking based on their proximity to an ideal solution representing optimal conditions. The results indicate that the Mazowiecki voivodeship leads the list in terms of renewable energy development potential, followed by Małopolskie i Pomorskie, and that this is mainly due to good economic conditions and large investments in renewable energy projects. Spatial autocorrelation analysis yielded a Moran’s I value of –0.1137 with a Z score of 0.303 and a p value of 0.752, suggesting a weak negative spatial autocorrelation that is not statistically significant. This implies that the distribution of renewable energy potential across voivodeships is largely random and is not influenced by spatial proximity. The study concludes that non-spatial factors play a more significant role in renewable energy development potential, offering valuable insights for policymakers and stakeholders to allow them to focus on economic and social variables when promoting renewable energy initiatives in Poland.
Concepts for the Sustainable Hydrometallurgical Processing of End-of-Life Lithium Iron Phosphate (LFP) Batteries
Authors: Müller Marius , Obuz Hüseyin Eren , Keber Sebastian , Tekmanli Firat , Mettke Luka Nils , Yagmurlu Bengi
Published: December 2024

Lithium-ion batteries with an LFP cell chemistry are experiencing strong growth in the global battery market. Consequently, a process concept has been developed to recycle and recover critical raw materials, particularly graphite and lithium. The developed process concept consists of a thermal pretreatment to remove organic solvents and binders, flotation for anode–cathode separation, and hydrometallurgical processes for product recovery. It has been shown that a pretreatment step is necessary for efficient flotation. By increasing the thermal treatment temperatures up to 450 °C, recovery rates of up to 73% are achieved. Similar positive effects are observed with leaching, where leaching efficiencies increase with higher treatment temperatures up to 400 °C. The results indicate that the thermal treatment of the black mass significantly influences both flotation and hydrometallurgical processes.
Predicting Climate Change Mitigation Behavior in Chinese Families: Effects of Metacognitive Climate Knowledge and Biospheric Values
Authors: BinQiang Jing , Ahrari Seyedali , Zaremohzzabieh Zeinab , Zarean Mansoureh , Abdullah Haslinda
Published: December 2024

(1) Background: Climate change mitigation behavior has become a critical area of research, particularly in understanding how family contexts influence such behaviors. However, there is limited empirical comparison of theoretical models explaining this behavior in Chinese families. (2) Purpose: This study aims to compare the effectiveness of three models—the Theory of Planned Behavior (TPB), the Value–Identity–Personal Norm Activation Model (VIP-NAM), and an extended TPB model—in predicting climate change mitigation behavior within Chinese families. (3) Methods: Data were collected from 302 Chinese families in Zhejiang Province. Partial least squares structural equation modeling was used to perform the data analysis in this study. (4) Results: The extended TPB model, incorporating biospheric values, personal norms, and perceived behavioral control, demonstrated superior explanatory and predictive capabilities compared to the TPB and VIP-NAM models. However, metacognitive climate knowledge did not significantly moderate the intention–behavior relationship. (5) Conclusion: The findings underscore the importance of integrating values and norms into theoretical frameworks for better understanding of climate change mitigation behavior. (6) Implications: This study provides insights into designing family-centered interventions and highlights the need to address the intention–behavior gap. Future research should explore additional contextual and cultural factors influencing these behaviors in Chinese families.
Nexus of Natural Resources, Renewable Energy, Capital Formation, Urbanization, and Foreign Investment in E7 Countries
Authors: Wang Zuyao , Xu Runguo
Published: December 2024

The global trend of rapid economic development and urbanization has created questions regarding the quality of the environment. In the group of emerging economies (E7), environmental challenges have intensified due to specific dynamics unique to these nations. This research is focused on determining the influence of urbanization (UBNZ), renewable energy (RWNE), capital formation (CPFR), foreign direct investment (FDIN), and natural resources (NTRR) on the ecological footprint (ECLF) of the E7 economies. The study employs the Panel Autoregressive Distributed Lag (PMG-ARDL) approach to examine these relationships, utilizing data spanning the period of 1990–2022. The results reveal that a 1% increase in the CPFR, NTRR, and UBNZ leads to increases in the ECLF of 0.0581%, 0.0263%, and 0.0299%, respectively. Conversely, a 1% increase in RWNE and FDIN reduces the ECLF by 0.0207% and 0.0556%, respectively, in the E7 economies. The study’s findings are further validated through robustness testing via the fully modified ordinary least squares (FMOLS) method. The study concludes with actionable policy recommendations aimed at enhancing environmental quality within these economies. These recommendations include promoting renewable energy adoption, attracting environmentally sustainable foreign investments, and implementing strategies to manage urbanization and natural resource use effectively.
Effects of Biochar Extract and Mineral Potassium Fulvic Acid on Salt Tolerance of Shanghai Bok Choy
Authors: Chai Hongxing , Wu Wenhong , Liu Yujiao , Wu Wei , Gao Peiling
Published: December 2024

The objective of this study was to compare the effects of biochar extract (BE) and mineral potassium fulvic acid (MPFA) on the salt tolerance and growth of Bok Choy (Shanghai Bok Choy) under saline conditions, aiming to utilize saline groundwater resources in the Yellow River Delta region. Based on the mineralization level of saline groundwater in the Yellow River Delta region, all the treatments were cultured in a nutrient solution containing 6 g·L−1 NaCl. Nine treatments were set up: BE (C1: 1 g, C2: 5 g, C3: 10 g, C4: 15 g·L−1), MPFA (H1: 0.2 g, H2: 0.4 g, H3: 0.6 g, H4: 0.8 g·L−1), and a control (CK). The results showed that both the BE and MPFA reduced MDA accumulation, increased SOD and CAT enzyme activities, thereby increasing the number of effective leaves, the maximum leaf length, and the maximum leaf width of Shanghai Bok Choy. However, MPFA was better than the BE at increasing the antioxidant enzyme activities and total chlorophyll content. Correlation analysis revealed that the hydrogen peroxide content and CAT enzyme activity were the most important factors affecting Shanghai Bok Choy yield. Compared to the CK, the BE treatments reduced the hydrogen peroxide content by 1.1–46.4% and increased the CAT enzyme activity by 4.1–68.0%, while the MPFA treatments increased the hydrogen peroxide content and decreased the CAT enzyme activity. Consequently, the fresh weight of Shanghai Bok Choy treated with the BE was 11.01–112.21% higher than the MPFA treatments and 5.17–49.70% higher than the CK. Overall, C3 had significantly lower hydrogen peroxide content than the CK and the highest CAT enzyme activity, which was markedly better than the MPFA treatments. This suggests that C3 may be an effective method for improving the salt tolerance and growth of Shanghai Bok Choy under salt stress. This study provides favorable data to support the solution to the problem of agricultural water use and the realization of sustainable development in the Yellow River Delta.
Food Confusion Between Edible and Poisonous Plants: A 22-Year Retrospective of the Southeastern France Poison Control Center
Authors: Torrents Romain , Reynoard Julien , Glaizal Mathieu , Schmitt Corinne , Von Fabeck Katharina , Boulamery Audrey , De Haro Luc , Simon Nicolas
Published: December 2024

Objective: In some regions of the globe, accidental food confusion regarding plants can cause severe poisoning events and deaths. The aim of this study was to report on those confusions from the Marseille Poison Control Centre’s (PCC) experience from 2002 to 2023. Results: Over 22 years, 2197 food confusion events were managed with 321 different species. The most frequently involved plant was Nerium oleander (289 cases, 13.1%), then Cucurbitaceae genus (3.3%), Colchicum autumnale (3.3%), Prunus amygdalus (3%), Mahinot esculenta (3%), Cytisus laburnum (2.6%), Aesculus hippocastanum (2.5%) and Narcissus Jonquilla (2%). Many botanical confusion events were also reported (n = 1386, 63%), but with fewer than five identical species. Only one death was reported for this review, in an event involving Aconitum napellus. Two antidotes were used for all the series: Datura genus and Prunus dulcis. Discussion: The most implicated plant was Nerium oleander. This is explained by its distribution. This rate is very low compared to that of suicide attempts with this plant. Many cases were symptomatic (53.6%), but very few of them described severe symptoms (only 0.5% severe poisonings). Few patients needed to be admitted to intensive care (0.4%), and even fewer needed an antidote (two cases). Only one death occurred, involving Aconitum napellus. Food confusions were more common than suicide attempts with plants, but seem to be less severe. However, death and serious complications can occur, so it is important to identify and manage the plants concerned. Material and Methods: For each food confusion event managed between 2002 and 2023 at the Southeastern France PCC based in Marseille, we performed a retrospective review. This PCC is responsible for the Provence–Alpes–Côte d’Azur Region, Corsica Island and Indian Ocean French overseas territories. For each case, severity was calculated with the Poison Severity Score (PSS).
Cosmologies with Perfect Fluids and Scalar Fields in Einstein’s Gravity: Phantom Scalars and Nonsingular Universes
Authors: Cimaglia Michela , Gengo Massimo , Pizzocchero Livio
Published: December 2024

In the initial part of this paper, we survey (in arbitrary spacetime dimension) the general FLRW cosmologies with non-interacting perfect fluids and with a canonical or phantom scalar field, minimally coupled to gravity and possibly self-interacting; after integrating the evolution equations for the fluids, any model of this kind can be described as a Lagrangian system with two degrees of freedom, where the Lagrange equations determine the evolution of the scale factor and the scalar field as functions of the cosmic time. We analyze specific solvable models, paying special attention to cases with a phantom scalar; the latter favors the emergence of nonsingular cosmologies in which the Big Bang is replaced, e.g., with a Big Bounce or a periodic behavior. As a first example, we consider the case with dust (i.e., pressureless matter), radiation, and a scalar field with a constant self-interaction potential (this is equivalent to a model with dust, radiation, a free scalar field and a cosmological constant in the Einstein equations). In the phantom subcase (say, with nonpositive spatial curvature), this yields a Big Bounce cosmology, which is a non-absurd alternative to the standard (ΛCDM) Big Bang cosmology; this Big Bounce model is analyzed in detail, even from a quantitative viewpoint. We subsequently consider a class of cosmological models with dust and a phantom scalar, whose self-potential has a special trigonometric form. The Lagrange equations for these models are decoupled passing to suitable coordinates (x,y), which can be interpreted geometrically as Cartesian coordinates in a Euclidean plane: in this description, the scale factor is a power of the radius r=x2+y2. Each one of the coordinates x,y evolves like a harmonic repulsor, a harmonic oscillator, or a free particle (depending on the signs of certain constants in the self-interaction potential of the phantom scalar). In particular, in the case of two harmonic oscillators, the curves in the plane described by the point (x,y) as a function of time are the Lissajous curves, well known in other settings but not so popular in cosmology. A general comparison is performed between the contents of the present work and the previous literature on FLRW cosmological models with scalar fields, to the best of our knowledge.
SARS-CoV-2 Infection of the Central Nervous System: A Case Report
Authors: Valkov Trifon , Argirova Radka , Dimitrov George
Published: December 2024

Central nervous system (CNS) infections caused by SARS-CoV-2 are uncommon. This case report describes the clinical progression of a 92-year-old female who developed a persistent neuroinfection associated with SARS-CoV-2. The patient initially presented with progressive fatigue, catarrhal symptoms, and a fever (38.6 °C). Initial laboratory findings revealed hypoxemia (O2 saturation 79.8%), acidosis (pH 7.3), an elevated C-reactive protein (CRP) level of 14.8 mg/L, and a high D-dimer level (2.15 µg/mL). Nasopharyngeal (NP) antigen and RT-PCR tests confirmed SARS-CoV-2 infection, and an NP swab also detected penicillin- and ampicillin-resistant Staphylococcus aureus. She was admitted for conservative management, including oxygen supplementation, IV fluids, and prophylactic anticoagulation. Subsequently, she developed neurological symptoms—lethargy, discoordination, and impaired communication—without signs of meningism. Cerebrospinal fluid (CSF) analysis identified SARS-CoV-2 RNA (Ct = 29) on RT-PCR, while bacterial cultures remained negative. Treatment was intensified to include 10% mannitol, dexamethasone, and empiric ceftriaxone. Despite these interventions, the patient remained somnolent, with a Glasgow Coma Scale (GCS) score of 10. Upon discharge, her GCS had improved to 14; however, she continued to experience lethargy and cognitive issues, commonly described as “brain fog”. Inflammatory markers remained elevated (CRP 23 mg/L) and repeat RT-PCR of CSF confirmed a persistent SARS-CoV-2 presence (Ct = 31). This case underscores the potential for SARS-CoV-2 to cause prolonged CNS involvement, leading to persistent neurological impairment despite standard therapy. Further research is essential to clarify the pathophysiology of and determine optimal management for SARS-CoV-2 neuroinfections.
Investigation of an Outbreak of Equine Herpesvirus-1 Myeloencephalopathy in a Population of Aged Working Equids
Authors: Pusterla Nicola , Lawton Kaila , Barnum Samantha , Ross Kelly , Purcell Kris
Published: December 2024

The objective of this study was to describe an outbreak of equine herpesvirus-1 myeloencephalopathy (EHM) in a population of aged equids. The outbreak was linked to the introduction of five healthy non-resident horses 15 days prior to the first case of acute recumbency. This fulminant EHM outbreak was predisposed by the grouping of the 33 unvaccinated animals in two large pens with shared water and feed troughs. Fourteen horses (42.4%) developed neurological deficits within the first week of the outbreak. Four additional equids developed fever and respiratory signs (EHV-1 infection), while fifteen horses remained healthy. EHM was supported by the detection of EHV-1 N752 in blood (n = 11) and/or nasal secretions (9). Three out of four equids with EHV-1 infection and two out of fifteen healthy horses tested qPCR-positive for EHV-1. All animals were managed in the field. EHM and EHV-1 equids were treated with a combination of antiherpetic, anti-inflammatory, and antithrombotic drugs. Six out of fourteen EHM horses (42.9%) were euthanized because of recumbence and the inability to stand with assistance or vestibular signs. Anti-EHV-1 total IgG and IgG 4/7 levels in acute serum samples showed no significant difference amongst the three disease groups (p > 0.05); however, antibody levels rose significantly between acute and convalescent serum samples for EHM (p = 0.0001) and EHV-1 equids (p = 0.02). This outbreak highlights a very high EHM attack and fatality rate in a population of aged equids and rapid spread of EHV-1, as the population shared common pens and feeding practices. The outbreak also showed that EHM cases can be managed in the field when referral to a hospital is not an option.
Viral Oncogenesis: Synergistic Role of Genome Integration and Persistence
Authors: La Frazia Simone , Pauciullo Silvia , Zulian Verdiana , Garbuglia Anna Rosa
Published: December 2024

Persistence is a strategy used by many viruses to evade eradication by the immune system, ensuring their permanence and transmission within the host and optimizing viral fitness. During persistence, viruses can trigger various phenomena, including target organ damage, mainly due to an inflammatory state induced by infection, as well as cell proliferation and/or immortalization. In addition to immune evasion and chronic inflammation, factors contributing to viral persistence include low-level viral replication, the accumulation of viral mutants, and, most importantly, maintenance of the viral genome and reliance on viral oncoprotein production. This review focuses on the process of genome integration, which may occur at different stages of infection (e.g., HBV), during the chronic phase of infection (e.g., HPV, EBV), or as an essential part of the viral life cycle, as seen in retroviruses (HIV, HTLV-1). It also explores the close relationship between integration, persistence, and oncogenesis. Several models have been proposed to describe the genome integration process, including non-homologous recombination, looping, and microhomology models. Integration can occur either randomly or at specific genomic sites, often leading to genome destabilization. In some cases, integration results in the loss of genomic regions or impairs the regulation of oncogene and/or oncosuppressor expression, contributing to tumor development.
Development of Multi-Temporal-Scales Precipitation-Type Separation Method in the Qinghai–Tibetan Plateau
Authors: Zhang Juan , Wang Weizhen , Che Tao , Su Xianfeng , Su Wenjiang
Published: December 2024

The accurate identification of precipitation types is very important for understanding the hydrological processes in cold regions. Existing identification methods have been established based on daily precipitation and meteorological data, which cannot match the high temporal resolution (such as hourly) simulations of hydrological processes. Based on the minutely surface meteorological data in the QTP from 2012 to 2021, we established three sub-models of the dynamic threshold method with wet-bulb temperature (Tw) and three sub-models of the frequency threshold method with air temperature (Ta) for distinguishing among precipitation types. The results revealed that the mean accuracy (ACC) of the three precipitation types was 0.86, and that these models provided a refined and accurate precipitation identification performance for the Qinghai–Tibet Plateau (QTP). However, these models performed well in the identification of rain and snowfall but performed poorly in the identification of sleet. In addition, the smaller the time scale and regional scales, the better the identification rate. In particular, snowfall is overestimated when daily precipitation-type separation thresholds are input into hourly or minute hydrological models. Therefore, to improve simulation performance, it is important to develop multi-temporal scale precipitation-type partitioning models, take regional variations into account when setting temperature thresholds, and conduct analyses at the finest possible time resolutions to minimize scale-related uncertainties.
Effects of Close-to-Nature Transformation of Plantations on Eco-Hydrological Function in Hainan Tropical Rainforest National Park
Authors: Yang Aohua , Li Guijing , Peng Wencheng , Wan Long , Song Xiqiang , Liu Yuguo , Nong Shouqian
Published: December 2024

Girdling is a crucial technique for promoting the close-to-nature transformation of plantation forests in Hainan Tropical Rainforest National Park (HNNP). It has shown effectiveness in aspects such as community structure and biodiversity restoration. However, its impacts on ecological functions like eco-hydrology still require further in-depth investigation. This study analyzes the impact of girdling on the eco-hydrological indices of three plantations—Acacia mangium, Pinus caribaea, and Cunninghamia lanceolata—through field investigations and laboratory tests. The data was evaluated using a game theory combination weighting-cloud model. The results show that the eco-hydrological indicators of leaf litter in A. mangium increased by 5.77% while those of P. caribaea and C. lanceolata decreased by 11.86% and 5.29%, respectively. Soil bulk density decreased slightly across all plantations while total porosity increased, with A. mangium showing the highest increase of 20.31%. Organic carbon content increased by 76.81% in A. mangium and 7.24% in C. lanceolata, whereas it decreased in P. caribaea. Saturated hydraulic conductivity increased by 33.32% in P. caribaea and 20.91% in A. mangium but decreased in C. lanceolata. Based on the cloud model, the eco-hydrological function of A. mangium improved from ‘medium’ to ‘good’, while that of P. caribaea and C. lanceolata declined towards the ‘poor’ level. In summary, during the process of close-to-nature transformation of tropical rainforests, girdling is an effective method to enhance the ecohydrological functions of broadleaf planted forests. However, for coniferous species, the ecohydrological functions of the planted forests weaken in the short term following the transformation.
Urban Single Precipitation Events: A Key for Characterizing Sources of Air Contaminants and the Dynamics of Atmospheric Chemistry Exchanges
Authors: Górka Maciej , Pilarz Aldona , Modelska Magdalena , Drzeniecka-Osiadacz Anetta , Potysz Anna , Widory David
Published: December 2024

The chemistry of atmospheric precipitation serves as an important proxy for discriminating the source(s) of air contaminants in urban environments as well as to discuss the dynamic of atmospheric chemistry exchanges. This approach can be undertaken at time scales varying from single events to seasonal and yearly time frames. Here, we characterized the chemical composition of two single rain episodes (18 July 2018 and 21 February 2019) collected in Wrocław (SW Poland). Our results demonstrated inner variations and seasonality (within the rain event as well as between summer and winter), both in ion concentrations as well as in their potential relations with local air contaminants and scavenging processes. Coupling statistical analysis of chemical parameters with meteorological/synoptic conditions and HYSPLIT back trajectories allowed us to identify three main factors (i.e., principal components; PC) controlling the chemical composition of precipitation, and that these fluctuated during each event: (i) PC1 (40%) was interpreted as reflecting the long-range transport and/or anthropogenic influences of emission sources that included biomass burning, fossil fuel combustion, industrial processes, and inputs of crustal origin; (ii) PC2 (20%) represents the dissolution of atmospheric CO2 and HF into ionic forms; and (iii) PC3 (20%) originates from agricultural activities and/or biomass burning. Time variations during the rain events showed that each factor was more important at the start of the event. The study of both SO42− and Ca2+ concentrations showed that while sea spray inputs fluctuated during both rain events, their overall impact was relatively low. Finally, below-cloud particle scavenging processes were only observed for PM10 at the start of the winter rain episode, which was probably explained by the corresponding low rain intensity and an overlap from local aerosol emissions. Our study demonstrates the importance of multi-time scale approaches to explain the chemical variability in rainwater and both its relation to emission sources and the atmosphere operating processes.
DFMM-Precip: Deep Fusion of Multi-Modal Data for Accurate Precipitation Forecasting
Authors: Li Jinwen , Wu Li , Liu Jiarui , Wang Xiaoying , Xue Wei
Published: December 2024

Weather forecasting is a classical problem in remote sensing, in which precipitation is difficult to predict accurately because of its complex physical motion. Precipitation significantly impacts human life, work, and the ecological environment. Precise precipitation forecasting is vital for water resource management, ecological protection, and disaster mitigation through precise precipitation forecasting. This study introduces an innovative deep learning-based precipitation-forecasting method DFMM-Precip that integrates reanalysis of precipitation data and satellite data using a multi-modal fusion layer and predicts future precipitation details through a global–local joint temporal-spatial attention mechanism. By effectively combining satellite infrared data with reanalysis data, the approach enhances the accuracy of precipitation forecasting. Experimental results for 24 h precipitation forecasts show that DFMM-Precip’s multi-modal fusion layer successfully integrates multi-modal data related to precipitation, leading to improved forecast accuracy. In particular, the global–local joint temporal-spatial attention mechanism provides precise, detailed forecasting of spatial and temporal precipitation patterns, outperforming other state-of-the-art models. The MSE of the forecasting results is 10 times lower than that of the advanced RNN model and 2.4 times lower than that of the advanced CNN model with single-modal data input. The probability of successful rainfall prediction is improved by more than 10%.