bluuebunny/crossref_metadata_2025 huggingface dataset:
In Situ and Ex Situ Bioremediation of Different Persistent Soil Pollutants as Agroecology Tool
Authors: Perez-Vazquez A. , Barciela P. , Prieto M. A.
Published: October 2024

Contamination or pollution of our environment has become a real global concern, especially in parallel with the increasing evolution of urbanization and industrialization, which in turn have released a plethora of different pollutants that end up being deposited in soils. It is crucial to investigate solutions that can minimize the extent of damage, and that are cost-effective, feasible and environmentally friendly, to treat a wide variety of contaminants in soils, as well as to detoxify various compounds. Bioremediation is a safe technique that has demonstrated satisfactory results and is easy to apply and maintain. This technique explores the degradation pathways of various biological agents (microorganisms, plants, algae, etc.) to neutralize contaminants. It is based on biodegradation through a complete mineralization of organic pollutants into inorganic innocuous compounds, such as carbon dioxide and water. This review aims to determine the feasibility of bioremediation as a cleanup technology for soils contaminated with pesticides, agrochemicals, chlorinated compounds, heavy metals, organic halogens, greenhouse gases, petroleum hydrocarbons, and many others, either in situ or ex situ. Different bioremediation approaches are described and compared, showing their advantages and drawbacks from a critical point of view. Moreover, both the economic and technical barriers of bioremediation are addressed, along with the outlook for the role of microorganisms in the process, the aim to identify future directions, and the application feasibility of this process.
Effects of Typical Supplementary Cementitious Materials (SCMs) on the Bonding Property of Cement with Casing under Shallow Formation of Deep-Water Environment
Authors: Ling Xingjie , Gu Tao , Zhang Gaoyin , Wang Ziyi , Wang Shilan , Zhang Lihua , Liu Laibao
Published: October 2024

Due to low temperature environments and other factors, the performance of cement slurry is affected by deep-water shallow formation cementing, which makes it difficult to ensure the interface bonding quality. The objective of this study is to investigate the effect of fly ash (FA), metakaolin (MK), and microsilica (MS) as supplementary cementitious materials (SCMs) by replacing 5–20% of the cement on the bonding property of cement with casing in the shallow formations of deep-water environments. This is based on the cementing conditions for the 20″ surface casing in the deep-water shallow formation of the LS18-1 well area. Under the 30 °C experimental conditions, the results showed that the compressive and bonding strength of cement with FA and MS decreased gradually as their dosage increased. In contrast, compared with the control group (BG0), the compressive strength of the MK group increased by 25.6%, 32.1%, and 24.2% under the optimal dosages (MK15, MK15, MK20) at 3, 7, and 14 days (d), and the bonding strength increased by 73.6% at 3 d (MK15) and 34.9% at 7 d (MK15). The test analysis showed that MK can promote cement hydration and generate more hydration products, which are conducive to cement-casing interface cementation. Additionally, it can improve the pore structure, reduce the fractal dimension of the pore volume, and ensure a more reasonable distribution of cement stone crack openings.
Effect of Rhizobacteria Application on Nutrient Content, Bioactive Compounds, Antioxidant Activity, Color Properties and Fruit Characteristics of Strawberry Cultivars
Authors: Elikara Alper Umut , Popescu Gheorghe Cristian , Demirel Serap , Sümbül Ahmet , Yaman Mehmet , Demirel Fatih , Say Ahmet , Güneş Adem
Published: October 2024

The aim of this study was to determine the effects of single and combined applications of plant-growth-promoting rhizobacteria (PGPR) bacteria on plant nutrition, biochemical content and fruit characteristics in Albion and Monterey strawberry cultivars. Bacillus subtilis OSU-142, Bacillus megaterium M3 and Paenibacillus polymyx were the PGPR used in the experiment. For each bacterial treatment, 10 mL of a 108 CFU mL−1 suspension was applied to the soil where Albion and Monterey cultivars were grown. PGPR bacteria were applied as single treatments and a mixture of equal amounts of these three bacterial species was applied as a mixed treatment. This study was carried out with a total of four different bacterial treatments and one control group. The highest fruit weight was obtained in the Monterey cultivar with 12.67 g in the Mix treatment and in the Albion cultivar with 11.79 g in the Bacillus megaterium M3 treatment. Regarding biochemical properties, Paenibacillus polymyxa was effective in influencing nutrient element content in fruits, while Bacillus subtilis OSU-142, Paenibacillus polymyxa and Bacillus megaterium M3 applications were more effective in leaf nutrient element content. It has been observed that the Mix treatment resulting from the combined use of bacteria, rather than their separate use, has a greater impact on fruit weight. Consequently, it has been understood that PGPR bacteria are potentially effective in improving the agronomic, pomological, and biochemical characteristics of strawberry cultivars and can be used in studies and breeding programs aimed at increasing strawberry yield and quality.
The Buddhist Logic of Distress (Saṃvega): An Exploration of Early Abhidharma Sources
Authors: Feinberg Nir
Published: October 2024

In the early Buddhist discourses, distressing experiences like fear and disgust are evaluated in contradictory modes. These upsetting emotions are considered both a detriment and an advantage for those seeking liberation from suffering. This ambivalence is reflective of the classical Buddhist conception of distress. The earliest scholastic Buddhist texts (i.e., Abhidharma sources) have sought to resolve the ambivalence surrounding this conception. The Abhidharma texts thus explain precisely how, when, where and for whom distress can prove favorable. By tracing this intellectual endeavor, I examine in this article the systematic and philosophical treatments of distress (saṃvega) in early Buddhist scholasticism. I outline the reasons for considering the experience of distress to be beneficial, unveiling the religious framework within which distress is rendered positive and even essential. My central claim is that early Abhidharma sources conceive of distress as a potential source of energy that facilitates one’s progress on the religious path and determines how rapidly one can understand the truth.
The Anthropocene, Self-Cultivation, and Courage: The Jesuit François Noël as a Witness of Inter-Religious Dialogue between Aristotelian and Confucian Ethics
Authors: Vendé Yves
Published: October 2024

This article explores the specific role of courage in the context of the Anthropocene’s moment; it first examines Aristotle’s conception of virtues, focusing on courage, before comparing it to Confucian thought and analyzing the historical dialogue between Western and Chinese traditions on ethics through the works of François Noël (1651–1729). Aristotle views moral cultivation as a social process wherein habits shape inner dispositions; in his view, courage is linked to other virtues, such as temperance and justice. For Aristotle, courage implies the appropriate balance between extremes and must be directed toward a worthy end, such as promoting positive change within a community. This Aristotelian perspective was later incorporated into a biblical framework by Aquinas and Suarez, emphasizing dichotomies between body and soul, as well as between humans and other living beings. These dichotomies must be challenged in the face of the Anthropocene’s emergencies. The second part of this contribution proceeds to a detour examining Confucian ethics, which rests on a different anthropology, emphasizing continuities rather than discontinuities. Like Aristotelian thought, Confucian thought also underscores moral education within a community; it prioritizes humanity, embodied through empathy and loyalty. In the Analects, courage is balanced by a sense of rituals and righteousness. Mencius further distinguishes several types of courage, stressing self-cultivation and the ruler’s responsibility to make empathetic, appropriate decisions for the community’s sake. From this perspective, courage is understood as the continuous perseverance in self-cultivation, coupled with a firm intention oriented toward the good of the community. Zhu Xi’s comments on Zilu’s courage in the Analects extend this Confucian tradition. Finally, this article highlights how a dialogue between Aristotelian and Confucian ethics began four centuries ago, particularly through Noël’s Philosophia Sinica, which combined these traditions. This inter-religious approach to ethics, enriched by figures such as Aquinas, Suarez, Zhu Xi, and neo-Confucian thinkers, requires re-evaluation because the understanding of personal ethics and nature has evolved. The modern naturalistic approach, with its emphasis on dichotomies, has contributed to a mechanistic view of nature, fostering its exploitation, and a devaluation of the body. This contrast highlights the urgent need for renewed dialogue between Western and Chinese ethical traditions to address contemporary challenges, with François Noël serving as a historical witness of these exchanges.
Suitable Method for Improving Friction Performance of Magnetic Wheels with Metal Yokes
Authors: Tanida Masaru , Ono Kosuke , Shiba Takehiro , Takada Yogo
Published: October 2024

A magnetic-wheeled robot is a type of robot that inspects large steel structures instead of humans, and it can run on a three-dimensional path by using wheels with built-in permanent magnets. For the robots to work safely, their magnetic wheels require both magnetic attractive forces and friction forces. Planetary-geared magnetic wheels, which we have developed, make direct contact with their yokes on the running surface to ensure their magnetic attractive force. However, this design decreases their frictional performance more than common magnetic wheels covered with soft materials. Therefore, the yokes require methods that can improve their frictional performance without decreasing their attractive force. To consider the best method for the use of magnetic wheels, this study has run experiments with five types of yokes, which have different processing. As a result, the yokes with corroded surfaces could have maintained the attractive force more than 90% of the time and increased their traction forces by about 36% in static conditions and about 30% in dynamic conditions compared to yokes with no machining. The main reasons for these experimental results are that the rust layer has stable irregularities on the surface and includes ferromagnetic materials.
Simulating High-Resolution Sun-Induced Chlorophyll Fluorescence Image of Three-Dimensional Canopy Based on Photon Mapping
Authors: Luo Yaotao , Xie Donghui , Qi Jianbo , Yan Guangjian , Mu Xihan
Published: October 2024

The remote sensing of sun-induced chlorophyll fluorescence (SIF) is an emerging technique with immense potential for terrestrial vegetation sciences. However, the interpretation of fluorescence data is often hindered by the complexity of observed land surfaces. Therefore, advanced remote sensing models, particularly physically based simulations, are critical to accurately interpret SIF data. In this work, we propose a three-dimensional (3D) radiative transfer model that employs the Monte Carlo ray-tracing technique to simulate the excitation and transport of SIF within plant canopies. This physically based approach can quantify the various radiative processes contributing to the observed SIF signal with high fidelity. The model’s performance is rigorously evaluated by comparing the simulated SIF spectra and angular distributions to field measurements, as well as conducting systematic comparisons with an established radiative transfer model. The results demonstrate the proposed model’s ability to reliably reproduce the key spectral and angular characteristics of SIF, with the coefficient of determination (R2) exceeding 0.98 and root mean square error (RMSE) being less than 0.08 mW m−2 sr−1 nm−1 for both the red and far-red fluorescence peaks. Furthermore, the model’s versatile representation of canopy structures, enabled by the decoupling of radiation and geometry, is applied to study the impact of 3D structure on SIF patterns. This capability makes the proposed model a highly attractive tool for investigating SIF distributions in realistic, heterogeneous canopy environments.
Coupled Effects of High Temperatures and Droughts on Forest Fires in Northeast China
Authors: Ma Bing , Liu Xingpeng , Tong Zhijun , Zhang Jiquan , Wang Xiao
Published: October 2024

High temperatures and droughts are two natural disasters that cause forest fires. During climate change, the frequent occurrence of high temperatures, droughts, and their coupled effects significantly increase the forest fire risk. To reveal the seasonal and spatial differences in the coupled effects of high temperatures and droughts on forest fires, this study used the Copula method and proposed the compound extremely high-temperature and drought event index (CTDI). The results indicated that the study area was subject to frequent forest fires in spring (71.56%), and the burned areas were mainly located in forests (40.83%) and the transition zone between farmland and forests (36.91%). The probability of forest fires in summer increased with high temperatures and drought intensity, with high temperatures playing a dominant role. The highest forest fire hazard occurred in summer (>0.98). The probability of a forest fire occurring under extreme meteorological conditions in summer and fall was more than twice as high as that in the same zone under non-extreme conditions. Droughts play a significant role in the occurrence and spread of forest fires during fall. These results can provide decision-making support for forest fire warnings and fire fighting in the Northeast China forest zone.
Detecting Drought-Related Temporal Effects on Global Net Primary Productivity
Authors: Luo Min , Meng Fanhao , Sa Chula , Bao Yuhai , Liu Tie , De Maeyer Philippe
Published: October 2024

Drought has extensive, far-reaching, and long-lasting asymmetric effects on vegetation growth worldwide in the context of global warming. However, to date, few scholars have attempted the systematic quantification of the temporal effects of drought on global vegetation across various vegetation types and diverse climate zones. Addressing this gap, we quantitatively investigated the effects of drought on global vegetation growth under various scenarios, considering lagged and cumulative effects as well as combined effects in the 1982–2018 period. Our investigation was based on long-term net primary productivity (NPP) and two multiple-timescale drought indices: the standardised precipitation index (SPI) and the standardised precipitation and evapotranspiration index (SPEI). Our main findings were the following: (1) SPI and SPEI exhibited lagged effects on 52.08% and 37.05% of global vegetation, leading to average time lags of 2.48 months and 1.76 months, respectively. The cumulative effects of SPI and SPEI were observed in 80.01% and 72.16% of global vegetated areas, respectively, being associated with relatively longer cumulative timescales of 5.60 months and 5.16 months, respectively. (2) Compared to the scenario excluding temporal effects, there were increases in the explanatory powers of SPI and SPEI for variations in vegetation NPP based on the lagged, cumulative, and combined effects of drought: SPI increased by 0.82%, 6.65%, and 6.92%, respectively, whereas SPEI increased by 0.67%, 5.73%, and 6.07%, respectively. The cumulative effects of drought on global vegetation NPP were stronger than the lagged effects in approximately two-thirds (64.95% and 63.52% for SPI and SPEI, respectively) of global vegetated areas. (3) The effects of drought on vegetation NPP varied according to climate zones and vegetation types. Interestingly, vegetation in arid zones was the most sensitive and resilient to drought, as indicated by its rapid response to drought and the longest cumulative timescales. The vegetation NPP in tropical and temperate zones exhibited a relatively stronger response to drought than that in cold and polar zones. The strongest correlation of vegetation NPP with drought occurred in shrubland areas, followed by grassland, cropland, forest, and tundra areas. Moreover, for each vegetation type, the correlations between vegetation NPP and drought differed significantly among most climate zones. (4) The vegetation NPP in warming-induced drought regions displayed a higher correlation to drought than that in non-warming-induced drought regions, with shorter lagged and longer cumulative timescales. Our findings highlight the heterogeneity of the lagged, cumulative, and combined effects of drought across various climate zones and vegetation types; this could enhance our understanding of the coupling relationship between drought and global vegetation.
Numerical Weather Prediction of Sea Surface Temperature in South China Sea Using Attention-Based Context Fusion Network
Authors: He Hailun , Shi Benyun , Zhu Yuting , Feng Liu , Ge Conghui , Tan Qi , Peng Yue , Liu Yang , Ling Zheng , Li Shuang
Published: October 2024

Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work, we developed a sophisticated deep learning model known as the Attention-based Context Fusion Network (ACFN). This model integrates an attention mechanism with a convolutional neural network framework. In this study, we applied the ACFN model to the South China Sea to evaluate its performance in predicting SST. The results indicate that for a 1-day lead time, the ACFN model achieves a Mean Absolute Error of 0.215 °C and a coefficient of determination (R2) of 0.972. In addition, in situ buoy data were utilized to validate the forecast results. The Mean Absolute Error for forecasts using these data increased to 0.500 °C for a 1-day lead time, with a corresponding R2 of 0.590. Comparative analyses show that the ACFN model surpasses traditional models such as ConvLSTM and PredRNN in terms of accuracy and reliability.
Andean Landscape Legacies: Comprehensive Remote Sensing Mapping and GIS Analysis of Long-Term Settlement and Land Use for Sustainable Futures (NW Argentina)
Authors: Lazzari Marisa , Oltean Ioana , Oyaneder Rodríguez Adrián , Scattolin María Cristina , Pereyra Domingorena Lucas
Published: October 2024

The Andes region has an exceptional record of high-altitude settlements integrated within widespread regional chains of mobility and exchange. The Sierra de Aconquija (NW Argentina, south-central Andes) is an effective climatic barrier that has afforded an enduring indigenous approach to land use, mobility, and exchange over millennia. Despite this rich history, the Sierra has been largely considered marginal in pre-Columbian regional cultural developments. Today, the expansion of extractive industries threatens the region’s heritage and the sustainable futures of local communities. Innovative, integrative methodologies are needed for landscape characterisation, heritage assessment, and sustainable policy development. Building on earlier work, we undertook the first comprehensive mapping of archaeological features over 3800 sq. km of the Sierra using interpreter-led assessment of commercial and open-access satellite imagery and DSM data, to verify earlier assumptions and to identify previously unnoticed trends in the aggregation, distribution, and connectivity of archaeological features. The mapping identified 6794 features distributed unevenly but with clear tendencies towards maximising topographic, ecologic, and connectivity advantages expressed consistently across the study area. The outcomes confirm the important role the Sierra had in pre-Hispanic times, highlighting the significance of ancient indigenous practices for the sustainability of vulnerable upland landscapes both in the Andes and worldwide.
Modeling of Biologically Effective Daily Radiant Exposures over Europe from Space Using SEVIRI Measurements and MERRA-2 Reanalysis
Authors: Czerwińska Agnieszka , Krzyścin Janusz
Published: October 2024

Ultraviolet solar radiation at the Earth’s surface significantly impacts both human health and ecosystems. A biologically effective daily radiant exposure (BEDRE) model is proposed for various biological processes with an analytical formula for its action spectrum. The following processes are considered: erythema formation, previtamin D3 synthesis, psoriasis clearance, and inactivation of SARS-CoV-2 virions. The BEDRE model is constructed by multiplying the synthetic BEDRE value under cloudless conditions by a cloud modification factor (CMF) parameterizing the attenuation of radiation via clouds. The CMF is an empirical function of the solar zenith angle (SZA) at midday and the daily clearness index from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements on board the second-generation Meteosat satellites. Total column ozone, from MERRA-2 reanalysis, is used in calculations of clear-sky BEDRE values. The proposed model was trained and validated using data from several European ground-based spectrophotometers and biometers for the periods 2014–2023 and 2004–2013, respectively. The model provides reliable estimates of BEDRE for all biological processes considered. Under snow-free conditions and SZA < 45° at midday, bias and standard deviation of observation-model differences are approximately ±5% and 15%, respectively. The BEDRE model can be used as an initial validation tool for ground-based UV data.
Evaluating ICESat-2 and GEDI with Integrated Landsat-8 and PALSAR-2 for Mapping Tropical Forest Canopy Height
Authors: Liu Aobo , Chen Yating , Cheng Xiao
Published: October 2024

Mapping forest canopy height is critical for climate modeling and forest management, and tropical forests present unique challenges for remote sensing due to their dense vegetation and complex structure. The advent of ICESat-2 and GEDI, two advanced lidar datasets, offers new opportunities for improving canopy height estimation. In this study, we used footprint-level canopy height products from ICESat-2 and GEDI, combined with features extracted from Landsat-8, PALSAR-2, and FABDEM products. The AutoGluon stacking ensemble learning algorithm was employed to construct inversion models, generating 30 m resolution continuous canopy height maps for the tropical forests of Puerto Rico. Accuracy validation was performed using the high-resolution G-LiHT airborne lidar products. Results show that tropical forest canopy height inversion remains challenging, with all models yielding relative root mean square errors (rRMSE) exceeding 0.30. The stacking ensemble model outperformed all base learners, and the GEDI-based map had slightly higher accuracy than the ICESat-2-based map, with RMSE values of 4.81 and 4.99 m, respectively. Both models showed systematic biases, but the GEDI-based model exhibited less underestimation for taller canopies, making it more suitable for biomass estimation. The proposed approach can be applied to other forest ecosystems, enabling fine-resolution canopy height mapping and enhancing forest conservation efforts.
Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye
Authors: Başakın Eyyup Ensar , Stoy Paul C. , Demirel Mehmet Cüneyd , Ozdogan Mutlu , Otkin Jason A.
Published: October 2024

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.
Estimation of Surface Water Level in Coal Mining Subsidence Area with GNSS RTK and GNSS-IR
Authors: Li Yunwei , Xu Tianhe , Guo Hai , Sun Chao , Liu Ying , Gao Guang , Miao Junwei
Published: October 2024

Ground subsidence caused by underground coalmining result in the formation of ponding water on the ground surface. Monitoring the surface water level is crucial for studying the hydrologic cycle in mining areas. In this paper, we propose a combined technique using Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) and GNSS Interferometric Reflectometry (GNSS-IR) to estimate the surface water level in areas of ground subsidence caused by underground coal mining. GNSS RTK is used to measure the geodetic height of the GNSS antenna, which is then converted into the normal height using the local height anomaly model. GNSS-IR is employed to estimate the height from the water surface to the GNSS antenna (or, the reflector height). To enhance the accuracy of the reflector height estimation, a weighted average model has been developed. This model is based on the coefficient of determination of the signal fitted by the Lomb-Scargle spectrogram and can be utilized to combine the reflector height estimations derived from multiple GNSS system and band reflection signals. By subtracting the GNSS-IR reflector height from the GNSS RTK-based normal height, the proposed method-based surface water level estimation can be obtained. In an experimental campaign, a low-cost GNSS receiver was utilized for the collection of dual-frequency observations over a period of 60 days. The collected GNSS observations were used to test the method presented in this paper. The experimental campaign demonstrates a good agreement between the surface water level estimations derived from the method presented in this paper and the reference observations.
Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain
Authors: Li Liangliang , Shi Yan , Lv Ming , Jia Zhenhong , Liu Minqin , Zhao Xiaobin , Zhang Xueyu , Ma Hongbing
Published: October 2024

The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, a novel infrared and visible image fusion method based on sparse representation and guided filtering in Laplacian pyramid (LP) domain is introduced. The source images are decomposed into low- and high-frequency bands by the LP, respectively. Sparse representation has achieved significant effectiveness in image fusion, and it is used to process the low-frequency band; the guided filtering has excellent edge-preserving effects and can effectively maintain the spatial continuity of the high-frequency band. Therefore, guided filtering combined with the weighted sum of eight-neighborhood-based modified Laplacian (WSEML) is used to process high-frequency bands. Finally, the inverse LP transform is used to reconstruct the fused image. We conducted simulation experiments on the publicly available TNO dataset to validate the superiority of our proposed algorithm in fusing infrared and visible images. Our algorithm preserves both the thermal radiation characteristics of the infrared image and the detailed features of the visible image.
An Object-Aware Network Embedding Deep Superpixel for Semantic Segmentation of Remote Sensing Images
Authors: Ye Ziran , Lin Yue , Dong Baiyu , Tan Xiangfeng , Dai Mengdi , Kong Dedong
Published: October 2024

Semantic segmentation forms the foundation for understanding very high resolution (VHR) remote sensing images, with extensive demand and practical application value. The convolutional neural networks (CNNs), known for their prowess in hierarchical feature representation, have dominated the field of semantic image segmentation. Recently, hierarchical vision transformers such as Swin have also shown excellent performance for semantic segmentation tasks. However, the hierarchical structure enlarges the receptive field to accumulate features and inevitably leads to the blurring of object boundaries. We introduce a novel object-aware network, Embedding deep SuperPixel, for VHR image semantic segmentation called ESPNet, which integrates advanced ConvNeXt and the learnable superpixel algorithm. Specifically, the developed task-oriented superpixel generation module can refine the results of the semantic segmentation branch by preserving object boundaries. This study reveals the capability of utilizing deep convolutional neural networks to accomplish both superpixel generation and semantic segmentation of VHR images within an integrated end-to-end framework. The proposed method achieved mIoU scores of 84.32, 90.13, and 55.73 on the Vaihingen, Potsdam, and LoveDA datasets, respectively. These results indicate that our model surpasses the current advanced methods, thus demonstrating the effectiveness of the proposed scheme.
Research on Radar Target Detection Based on the Electromagnetic Scattering Imaging Algorithm and the YOLO Network
Authors: Guo Guangbin , Wang Rui , Guo Lixin
Published: October 2024

In this paper, the radar imaging technology based on the time-domain (TD) electromagnetic scattering algorithm is used to generate image datasets quickly and apply them to target detection research. Considering that radar images are different from optical images, this paper proposes an improved strategy for the traditional You Only Look Once (YOLO)v3 network to improve target detection accuracy on radar images. The speckle noise in radar images can cover the real information of a target image and increase the difficulty of target detection. The attention mechanisms are added to the traditional YOLOv3 network to strengthen the weight of the target region. By comparing the target detection accuracy under different attention mechanisms, an attention module with higher detection accuracy is obtained. The validity of the proposed detection network is verified on a simulation dataset, a measured real dataset, and a mixed dataset. This paper is about an interdisciplinary study of computational electromagnetics, remote sensing, and artificial intelligence. Experiments verify that the proposed composite network has better detection performance.
A Study on the Predawn Ionospheric Heating Effect and Its Main Controlling Factors
Authors: Tang Zhenzhang , Le Huijun , Liu Libo , Chen Yiding , Zhang Ruilong , Li Wenbo , Liu Wendong
Published: October 2024

We proposed, for the first time, that the angle between the horizontal projection of the magnetic field line and the sunrise line (AMFS) is a crucial factor that controls predawn heating. Through quantitative analysis, we determined that both the AMFS and the length of the magnetic field line (LMF) significantly affect predawn heating. We found that an increased AMFS intensifies predawn heating, while an increased LMF counteracts it. Our research indicates that the optimal conditions for peak predawn heating occur at an AMFS of approximately 30 degrees and an LMF of about 4000 km, where the effect surpasses 400 K. Additionally, we observed that the effects of both the AMFS and the LMF on predawn heating exhibit a saturation effect. This study provides a clearer understanding of the factors driving predawn ionospheric heating, with implications for topside ionosphere research.
A Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging
Authors: Wang Xiaorui , Zhang Chao , Qiang Zhenping , Liu Chang , Wei Xiaojun , Cheng Fengyun
Published: October 2024

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.
Supercritical Extraction of Ylang Ylang (Cananga odorata) Essential Oil at the Near-Critical Region
Authors: Mahabir Rodney , Maharaj Sharad , Watson Marian J. , McGaw David R. , Coonai Cian
Published: October 2024

The flowers of the ylang ylang tree contain an essential oil which is utilized in high-quality perfumes. The traditional mode of extraction is by steam distillation but it has been shown that the more modern supercritical fluid extraction (SFE) using carbon dioxide has potential for replacing steam distillation. This technology, however, generally operates under high pressures, up to 500 bar. The work described in this paper examines the possibility of using carbon dioxide at much lower pressures, close to the critical point, i.e., 75 bar and 30 °C. Two series of experiments were therefore carried out under such conditions, the first using carbon dioxide alone and the second utilizing ethanol as a co-solvent, the conditions being chosen by applying the Design of Experiments (DOE) technique over ranges of pressure from 80 to 120 bar and temperatures from 35 to 50 °C. Extraction curves are presented which show the rates of extraction to be significantly increased by the use of the co-solvent, with the measured values being 0.74% to 0.97% with no co-solvent addition, increasing to 0.92% to 1.16% with co-solvent addition. These rates are, however, lower than the rates previously reported at higher pressures, i.e., 0.9 to 1.8%. Better quality oils are, however, produced compared to those at higher pressures, with the major components being benzene benzoate, benzene salicylate, cubebene, and benzyl acetate. It is recommended that an economic study be carried out to evaluate whether it is feasible to utilize this process commercially.
What Are Sporty Sisters Talking About? A Textual Analysis of an All-Female Athlete Reality Show
Authors: Kim Yunjung
Published: October 2024

This study explores the representation of female athletes cast in a South Korean reality show titled Sporty Sisters (Korean title: Nonun Unni). Though there have been attempts to understand the media representation of female athletes from diverse media landscapes, the analysis of Asian female athletes is limited. Such interpretations have been discussed through geopolitical relations or nationalistic representations, which lack in-depth understanding and exploration of Asian athletes. Therefore, this paper expands the narrative by analyzing the first season (54 episodes) of Sporty Sisters. It is observed that professional athletes who transition to the entertainment industry are identified as “spor-tainers” and implicitly follow norms and rules applied to public figures. In this reality show, female athletes express their opinions on marriage, menstruation, pregnancy, and domestic roles while displaying characteristics of a girl crush, manifesting a variety of the female attributes of sportswomen. Despite the social expectations to fulfill their roles as sports celebrities and Korean women, Sporty Sisters is an outlet for female athletes to an extent to freely expose and describe their experiences as athletes and individuals.
Physical Therapy in Neurorehabilitation with an Emphasis on Sports: A Bibliometric Analysis and Narrative Review
Authors: Pamboris George M. , Plakias Spyridon , Tsiakiri Anna , Karakitsiou Georgia , Bebeletsi Paschalina , Vadikolias Konstantinos , Aggelousis Nikolaos , Tsiptsios Dimitrios , Christidi Foteini
Published: October 2024

The increasing interest in physical therapy in sports neurorehabilitation stems from the high incidence of neurological injuries among athletes and the crucial role of rehabilitation in facilitating their safe return to sports. This study aims to provide a comprehensive analysis of research trends in physical therapy and neurorehabilitation in athletes. This study presents a bibliometric analysis of 103 documents from the Scopus database, followed by a narrative review of the identified thematic areas. Together, these approaches offer a comprehensive overview of the international literature on the application of physical therapy in sports neurorehabilitation, highlighting key trends and contributors. The software VOSviewer and Power BI (2.136.1202.0) were used for the bibliometric analysis and the visualization of the results. Techniques such as performance analysis (documents per year, top sources and countries in documents, and top authors in citations) and science mapping (co-authorship, bibliographic coupling, co-citation, and co-occurrence) were conducted. The results revealed the journals and the authors with the greatest impact in the field and collaborations between various countries. From the co-occurrence analysis of the keywords, three key thematic clusters were identified, Clinical Approaches and Outcomes in Neurorehabilitation, Athlete-Centered Neurorehabilitation Techniques, and Specialized Interventions in Sports Medicine and Neurorehabilitation, which were used to conduct the narrative review. These findings provide a solid foundation for future research and clinical practice aimed at enhancing recovery times and overall performance in athletes with neurological injuries.
Mild Solutions of the (ρ1,ρ2,k1,k2,φ)-Proportional Hilfer–Cauchy Problem
Authors: Wang Haihua , Zhao Jie
Published: October 2024

Inspired by prior research on fractional calculus, we introduce new fractional integral and derivative operators: the (ρ1,ρ2,k1,k2,φ)-proportional integral and the (ρ1,ρ2,k1,k2,φ)-proportional Hilfer fractional derivative. Numerous previous studied fractional integrals and derivatives can be considered as particular instances of the novel operators introduced above. Some properties of the (ρ1,ρ2,k1,k2,φ)-proportional integral are discussed, including mapping properties, the generalized Laplace transform of the (ρ1,ρ2,k1,k2,φ)-proportional integral and (ρ1,ρ2,k1,k2,φ)-proportional Hilfer fractional derivative. The results obtained suggest that the most comprehensive formulation of this fractional calculus has been achieved. Under the guidance of the findings from earlier sections, we investigate the existence of mild solutions for the (ρ1,ρ2,k1,k2,φ)-proportional Hilfer fractional Cauchy problem. An illustrative example is provided to demonstrate the main results.
Enhancing Conductivity of Nickel Oxide to Achieve Scalable Preparation for High-Efficiency Perovskite Solar Modules
Authors: Wang Lei , Li Xiaobo , Yuan Shihao , Qian Feng , Kang Zhangli , Li Shibin
Published: October 2024

NiOx, prepared via the sputtering method, exhibits low conductivity and energy level mismatch with the perovskite layer, thereby limiting further enhancements in the performance of perovskite solar modules (PSMs). Unlike traditional methods that enhance the performance of NiOx through reactive sputtering or directly doping NiOx targets with metal ions, both of which incur high costs and low efficiency, we employ an evaporation method using LiF to achieve efficient and low-cost doping of NiOx. Compared to the pristine NiOx, the incorporation of LiF significantly increases the conductivity of NiOx. Additionally, the incorporation of LiF enhances the quality of the deposited perovskite films, as well as the energy level alignment and symmetry between NiOx and the perovskite, effectively improving the hole extraction and transport capabilities between NiOx and the perovskite. As a result, the PSM (active area of 57.30 cm²) fabricated in air achieves an impressive efficiency of 19.54%. Furthermore, the unencapsulated PSM retains 80% of its initial efficiency after 700 h of continuous illumination, whereas the NiOx-based PSM drops to 80% after only 150 h. This study provides a simple and low-cost method for doping NiOx, which is of great significance for the further industrialization of PSMs.
Predicting Dependent Edges in Nonequilibrium Complex Systems Based on Overlapping Module Characteristics
Authors: Zou Qingyu , Yan Lin , Gong Yue , Hou Jingfei
Published: October 2024

Problem: Predicting dependency relationships in nonequilibrium systems is a critical challenge in complex systems research. Solution proposed: In this paper, we propose a novel method for predicting dependent edges in network models of nonequilibrium complex systems, based on overlapping module features. This approach addresses the many-to-many dependency prediction problem between nonequilibrium complex networks. By transforming node-based network models into edge-based models, we identify overlapping modular structures, enabling the prediction of many-to-many dependent edges. Experimental evaluation: This method is applied to dependency edge prediction in power and gas networks, curriculum and competency networks, and text and question networks. Results: The results indicate that the proposed dependency edge prediction method enhances the robustness of the network in power–gas networks, accurately identifies supporting relationships in curriculum–competency networks, and achieves better information gain in text–question networks. Conclusion: These findings confirm that the overlapping module-based approach effectively predicts dependencies across various nonequilibrium complex systems in diverse fields.
A Real-Time and Online Dynamic Reconfiguration against Cyber-Attacks to Enhance Security and Cost-Efficiency in Smart Power Microgrids Using Deep Learning
Authors: Yaghoubi Elnaz , Yaghoubi Elaheh , Yusupov Ziyodulla , Maghami Mohammad Reza
Published: October 2024

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.
Doses of Botulinum Toxin in Cervical Dystonia: Does Ultrasound Guidance Change Injection Practices?
Authors: Kreisler Alexandre , Mortain Léa , Watel Kaëlig , Mutez Eugénie , Defebvre Luc , Duhamel Alain
Published: October 2024

Background: Cervical dystonia is widely understood to benefit from botulinum toxin injections. The injection practices may be influenced by specific factors, including the method of injection. Three main guidance methods can be used: palpation of anatomical landmarks, ultrasound, and electromyography. We investigated how target muscles and doses of botulinum toxin were modified after the transition from surface anatomy (non-guided) to ultrasound (US-guided), in patients with cervical dystonia. We also determined the long-term dose trend. Methods: We studied a group of 82 patients, who received non-guided injections (median: 16.5 cycles/5.1 years) followed by US-guided injections (median: 12.0 cycles/3.8 years). Results: More muscles, and especially deep muscles, were injected during the US-guided period. The total dose and number of injected muscles were higher when US guidance was used, but the mean dose per muscle was lower. Over the long term, the total dose stabilized, and the mean dose per muscle decreased during the US-guided period. Conclusions: According to our results, the guidance method has a strong impact on the botulinum toxin injection strategy in cervical dystonia (target muscles and dose). Also, the treatment appeared more stable when using US guidance; this could be explained by the good precision of such injections.