bluuebunny/crossref_metadata_2025 huggingface dataset:
Review on PLGA Polymer Based Nanoparticles with Antimicrobial Properties and Their Application in Various Medical Conditions or Infections
Authors: Shakya Ashok K. , Al-Sulaibi Mazen , Naik Rajashri R. , Nsairat Hamdi , Suboh Sara , Abulaila Abdelrahman
Published: August 2023

The rise in the resistance to antibiotics is due to their inappropriate use and the use of a broad spectrum of antibiotics. This has also contributed to the development of multidrug-resistant microorganisms, and due to the unavailability of suitable new drugs for treatments, it is difficult to control. Hence, there is a need for the development of new novel, target-specific antimicrobials. Nanotechnology, involving the synthesis of nanoparticles, may be one of the best options, as it can be manipulated by using physicochemical properties to develop intelligent NPs with desired properties. NPs, because of their unique properties, can deliver drugs to specific targets and release them in a sustained fashion. The chance of developing resistance is very low. Polymeric nanoparticles are solid colloids synthesized using either natural or synthetic polymers. These polymers are used as carriers of drugs to deliver them to the targets. NPs, synthesized using poly-lactic acid (PLA) or the copolymer of lactic and glycolic acid (PLGA), are used in the delivery of controlled drug release, as they are biodegradable, biocompatible and have been approved by the USFDA. In this article, we will be reviewing the synthesis of PLGA-based nanoparticles encapsulated or loaded with antibiotics, natural products, or metal ions and their antibacterial potential in various medical applications.
Performance Characteristics of Silicone Rubber for Use in Acidic Environments
Authors: Zhang Zhijin , Zhang Zhiqin , Yue Song , Jiang Xingliang , Hu Jianlin
Published: August 2023

Silicone rubber insulators are widely used in power grids because of their excellent performance, but aging has been an inevitable problem of silicone rubber, especially in extreme conditions, such as acidic conditions. In order to clarify the performance changes in silicone rubber in an acidic environment, this paper uses the developed acid-resistant silicone rubber sheet and common silicone rubber samples as the research objects, and conducts an aging comparison test on them in a natural acidic environment. The electrical properties, physical properties, and chemical properties of the two types of silicone rubber specimens with different aging times are analyzed to obtain the performance characteristics of silicone rubber under a natural acidic environment. The research results show that the dry flash voltage and pollution flashover voltage of the acid-resistant silicone rubber after one year of aging are greater than those of the common type; the water repellency of both types of silicone rubber remains in good condition. The silicone rubber produced by our team according to the self-developed acid-resistant silicone rubber formula has indeed played a role in delaying aging in an acidic environment compared with the common-type silicone rubber.
Effect of Glycerol as Processing Oil in Natural Rubber/Carbon Black Composites: Processing, Mechanical, and Thermal Aging Properties
Authors: Naebpetch Weerawut , Thumrat Sutiwat , Indriasari  , Nakaramontri Yeampon , Sattayanurak Suppachai
Published: August 2023

The present work aims to study the effect of glycerol as a replacement for mineral oils in natural rubber (NR) composites to obtain suitable properties via cure characteristics, mechanical properties, and thermal stability. Glycerol was used at a 5 phr rate in the compound with carbon black as a reinforcing filler and was compared to mineral processing oils such as aromatic oil, treated distillate aromatic extracted oil, and paraffinic oil. Compared to the other oils, glycerol exhibits better maximum torque and torque differences. Also, a shorter scorch time, cure time, and a higher cure rate index of the compounds were observed. However, although the received mechanical properties, including tensile strength, elongation at break, and compression set of the vulcanized rubber using glycerol showed slightly lower values than the others, the 100% and 300% moduli, as well as the hardness of the composites filled with glycerol, exhibit better values relative to the other commercial oils. These findings demonstrate that glycerol overall presents a good balance of properties, making it beneficial to use glycerol as a substitute for mineral oil in tire, shoe sole, and rubber stopper applications.
A Critical Review of Palladium Nanoparticles Decorated in Smart Microgels
Authors: Arif Muhammad
Published: August 2023

Palladium nanoparticles (Pd) combined with smart polymer microgels have attracted significant interest in the past decade. These hybrid materials have unique properties that make them appealing for various applications in biology, environmental remediation, and catalysis. The responsive nature of the microgels in these hybrids holds great promise for a wide range of applications. The literature contains diverse morphologies and architectures of Pd nanoparticle-based hybrid microgels, and the architecture of these hybrids plays a vital role in determining their potential uses. Therefore, specific Pd nanoparticle-based hybrid microgels are designed for specific applications. This report provides an overview of recent advancements in the classification, synthesis, properties, characterization, and uses of Pd nanostructures loaded into microgels. Additionally, the report discusses the latest progress in biomedical, catalytic, environmental, and sensing applications of Pd-based hybrid microgels in a tutorial manner.
Investigation of the Effects of Polyurethane-Modified Polycarboxylate at Ambient Temperature on the Characteristics of Cement with Supplementary Cementitious Materials
Authors: Xiang Shuncheng , Zheng Tingxiang , Zhang Jiake , Jiang Zhen , Liu Bin , Huang Liangjun
Published: August 2023

Via radical polymerization, three polyurethane-modified polycarboxylate molecules of various comb topologies were synthesized. This study investigated the effects of varying types and concentrations of supplementary cementitious materials (SCMs) on the surface tension, flowability, and zeta potential of cement. An elevation in the molar ratio between isoamyl alcohol polyoxyethylene (TPEG) and acrylic acid (AA) from 1:1 to 5:1 reduced the surface tension of the polycarboxylate molecule from 47.70 mN/m to 35.53 mN/m and increased flowability from 280 mm to 310 mm, as the results indicated. An increase in the SCM and polycarboxylate dosage proportionally decreased liquid-phase surface tension and increased flowability. A decrease in the water-to-cement (w/c) ratio from 0.5 to 0.3 corresponded to an observed increase in the zeta potential of cement pastes. However, a rise in the quantity of polycarboxylate and SCMs corresponded to a decrease in the zeta potential at a w/c ratio of 0.3.
From Basics to Frontiers: A Comprehensive Review of Plasma-Modified and Plasma-Synthesized Polymer Films
Authors: Dufour Thierry
Published: August 2023

This comprehensive review begins by tracing the historical development and progress of cold plasma technology as an innovative approach to polymer engineering. The study emphasizes the versatility of cold plasma derived from a variety of sources including low-pressure glow discharges (e.g., radiofrequency capacitively coupled plasmas) and atmospheric pressure plasmas (e.g., dielectric barrier devices, piezoelectric plasmas). It critically examines key operational parameters such as reduced electric field, pressure, discharge type, gas type and flow rate, substrate temperature, gap, and how these variables affect the properties of the synthesized or modified polymers. This review also discusses the application of cold plasma in polymer surface modification, underscoring how changes in surface properties (e.g., wettability, adhesion, biocompatibility) can be achieved by controlling various surface processes (etching, roughening, crosslinking, functionalization, crystallinity). A detailed examination of Plasma-Enhanced Chemical Vapor Deposition (PECVD) reveals its efficacy in producing thin polymeric films from an array of precursors. Yasuda’s models, Rapid Step-Growth Polymerization (RSGP) and Competitive Ablation Polymerization (CAP), are explained as fundamental mechanisms underpinning plasma-assisted deposition and polymerization processes. Then, the wide array of applications of cold plasma technology is explored, from the biomedical field, where it is used in creating smart drug delivery systems and biodegradable polymer implants, to its role in enhancing the performance of membrane-based filtration systems crucial for water purification, gas separation, and energy production. It investigates the potential for improving the properties of bioplastics and the exciting prospects for developing self-healing materials using this technology.
Unveiling the Latest Breakthroughs in Menaquinone-7 Research through Fermentation-Based Production
Authors: Berenjian Aydin , Yazdanpanah Nima
Published: August 2023

MK-7, like other biological molecules, exists in geometric isomers, including cis and trans forms, among which only the all-trans form holds biological significance. Recent studies have drawn attention to the manifold health advantages linked to the consumption of menaquinone-7 (MK-7). Nonetheless, the scarcity of MK-7 in natural dietary sources underscores the necessity for creating dietary supplements to fulfil daily intake requisites. Obtaining MK-7 involves employing production techniques encompassing solid- or liquid-state fermentation. However, upscaling this process becomes intricate in static fermentation due to challenges in heat and mass transfer. Consequently, the bulk of research on MK-7 synthesis via fermentation has concentrated on the liquid-state approach. To this end, endeavors have been dedicated to refining MK-7 biosynthesis by exploring diverse fermentation media compositions, optimal growth conditions, and even integrating nanobiotechnology methodologies. Innovative biofilm reactors, capable of facilitating biofilm attachment on plastic composite substrates, have also emerged as a promising solution, particularly when utilizing B. subtilis cells. The biofilm reactors exhibit robust extracellular MK-7 secretion, effectively surmounting the hurdles posed by high aeration and agitation rates. However, a demonstration of the scalability of this technology to pilot and industrial scales is still pending. This work offers an outline of the latest advancements in MK-7 research, with a specific focus on the strides made in MK-7 production through fermentation techniques. The paramount importance of the all-trans form of MK-7 is underscored, accentuating its role in enhancing human well-being. The ramifications of this work hold the potential to pave the way for novel strategies to amplify MK-7 production and formulate products with an optimized MK-7 profile, thereby promising avenues for enhancing human health and nutrition.
Revisiting Secularization in Light of Growing Diversity: The European Case
Authors: Davie Grace
Published: August 2023

This article brings together two rather different trends in the religious life of twenty-first century Europe. On the one hand, secularization continues—faster in some places than others and with varying implications for the society in question. On the other, Europe—and especially western Europe—is becoming increasingly diverse, an equally inexorable development brought about by immigration. Is it possible to reconcile the two, keeping in mind that secularization erodes religious literacy, thus impeding constructive conversation about religion in public life, whereas the management of religious diversity demands this capacity on an almost daily basis? All too often the result is an ill-informed and ill-mannered debate. Can anything be done? Is it possible, in other words, to encourage a better conversation about religion in this part of the world? Understanding the religious dimensions of the current conflict in Ukraine raises similar—but distinctive—issues; they are central to the underlying discussion.
A Narrative Approach to Discerning Some Key Issues for Catholic Education in a More Synodal Church
Authors: Rymarz Richard
Published: August 2023

Synodality is described as a mentality that esteems three actions: encountering, listening. and discerning. This mentality is applied to a discourse on Catholic education premised on three illustrative narratives. Narratives are used as a suitable instrument to gain deep insights into the lived experience of teachers working in Catholic schools. Several concurrent themes are identified as emerging issues for a more synodal church. These include challenges in providing enough teachers to animate the religious identity of schools. This is linked to profound changes in patterns of religious affiliation that have been evident for several decades. Considering this, acknowledgement needs to be made that the lived experience of teachers entering Catholic education today is distinctive and different from previous generations. In light of this, a number of points of discernment are made. These include the need for ongoing, nuanced teacher recruitment and formation, as well as a reconceptualization of religious education (RE) to take into account changing cultural dynamics and providing more practical support for teachers, especially RE teachers.
Improving Shallow Water Bathymetry Inversion through Nonlinear Transformation and Deep Convolutional Neural Networks
Authors: Sun Shuting , Chen Yifu , Mu Lin , Le Yuan , Zhao Huihui
Published: August 2023

Nearshore bathymetry plays an essential role in various applications, and satellite-derived bathymetry (SDB) presents a promising approach due to its extensive coverage and comprehensive bathymetric map production capabilities. Nevertheless, existing retrieval techniques, encompassing physics-based and pixel-based statistical methodologies such as support vector regression (SVR), band ratio, and Kriging regression, exhibit limitations stemming from the intricate water reflectance process and the under-exploitation of the spatial component inherent in SDB. To surmount these obstacles, we introduce employment of deep convolutional networks (DCNs) for SDB in this study. We assembled multiple scenes utilizing networks with varying scale emphasis and an assortment of satellite datasets characterized by distinct spatial and spectral resolutions. Our findings reveal that these deep learning models yield high-caliber bathymetry outcomes, with nonlinear normalization further mitigating residuals in shallow water regions and substantially enhancing retrieval performance. A comparative analysis with the prevalent SVR technique substantiates the efficacy of the proposed methodology.
Interframe Saliency Transformer and Lightweight Multidimensional Attention Network for Real-Time Unmanned Aerial Vehicle Tracking
Authors: Deng Anping , Han Guangliang , Chen Dianbing , Ma Tianjiao , Wei Xilai , Liu Zhichao
Published: August 2023

UAV visual-object-tracking technology based on Siamese neural networks has great scientific research and practical application value, and it is widely used in geological surveying, reconnaissance monitoring, and environmental monitoring. Due to the limited onboard computational resources and complex real-world environments of drones, most of the existing tracking systems based on Siamese neural networks struggle to combine excellent performance with high efficiency. Therefore, the key issue is to study how to improve the accuracy of target tracking under the challenges of real-time performance and the above factors. In response to this problem, this paper proposes a real-time UAV tracking system based on interframe saliency transformer and lightweight multidimensional attention network (SiamITL). Specifically, interframe saliency transformer is used to continuously perceive spatial and temporal information, making the network more closely related to the essence of the tracking task. Additionally, a lightweight multidimensional attention network is used to better capture changes in both target appearance and background information, improving the ability of the tracker to distinguish between the target and background. SiamITL is effective and efficient: extensive comparative experiments and ablation experiments have been conducted on multiple aerial tracking benchmarks, demonstrating that our algorithm can achieve more robust feature representation and more accurate target state estimation. Among them, SiamITL achieved success and accuracy rates of 0.625 and 0.818 in the UAV123 benchmark, respectively, demonstrating a certain level of leadership in this field. Furthermore, SiamITL demonstrates the potential for real-time operation on the embedded platform Xavier, highlighting its potential for practical application in real-world scenarios.
A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence
Authors: Mei Ruyu , Mao Kebiao , Shi Jiancheng , Nielson Jeffrey , Bateni Sayed M. , Meng Fei , Du Guoming
Published: August 2023

Retrieval of integrated water vapor content (WVC) from remote sensing data is often ill-posed because of insufficient observational information. There are many factors that cause WVC changes, which yield instability in the accuracy of many traditional algorithms. To overcome this problem, we developed a novel fully-coupled paradigm for the robust retrieval of WVC from thermal infrared remote sensing data. Through the derivation of the physical radiative transfer equation, we determined two conditions that need to be satisfied for the deep learning retrieval paradigm of WVC. The first condition is that the input parameters and output parameters of the deep learning need to be able to build a complete set of solvable equations in theory. The second condition is that, if there is a strong relationship between input parameters and output parameters, it can be directly retrieved. If it is a weak relationship, we need to use prior knowledge to improve the portability and accuracy of the algorithm. The training and test data of deep learning is composed of representative solutions of physical methods and solutions of statistical methods. The representative solutions of the physical methods were obtained from the physical forward model, and the statistical solutions were obtained from multi-source data which can compensate for the defect that the physical model cannot simulate mixed pixels. MODIS L1B data was used for case analysis of paradigm retrieval, and the analysis indicated that four thermal infrared bands were usually needed as the input parameters of deep learning and the integrated water vapor content as the output parameter. When land surface temperature and emissivity were taken as prior knowledge, the root-mean-square error (RMSE) of the retrieved WVC was 0.07 g/cm2. The optimal accuracy RMSE was 0.27 g/cm2. When there was a strong correlation between input parameters and output parameters, i.e., if there were two bands that were very sensitive to WVC in the band combination, high-precision retrieval could also be achieved without prior knowledge. All the analyses show that the paradigm of deep learning coupling physics and statistics can accurately retrieve WVC, which is a significant improvement on the traditional method and solves the problem of lack of physical interpretation of deep learning.
Three-Dimensional Reconstruction and Geometric Morphology Analysis of Lunar Small Craters within the Patrol Range of the Yutu-2 Rover
Authors: Xu Xinchao , Fu Xiaotian , Zhao Hanguang , Liu Mingyue , Xu Aigong , Ma Youqing
Published: August 2023

Craters on the lunar surface are the most direct method for the study of geological processes and are of great significance to the study of lunar evolution. In order to fill the research gap on small craters (diameter less than 3 m), we focus on the small craters around the moving path of the Yutu-2 lunar rover and carry out a 3D reconstruction and geometrical morphology analysis on them. First, a self-calibration model with multiple feature constraints is used to calibrate the navigation camera and obtain the internal and external parameters. Then, the sequence images with overlapping regions from neighboring stations are used to obtain the precise position of the rover through the bundle adjustment (BA) method. After that, a cross-scale cost aggregation for a stereo matching network is proposed to obtain a parallax map, which can further obtain 3D point clouds of the lunar surface. Finally, the indexes of the craters are extracted (diameter D, depth d, and depth–diameter ratio dr), and the different indicators are fitted and analyzed. The results suggest that CscaNet has an anomaly percentage value of 1.73% in the KITTI2015 dataset, and an EPE of 0.74 px in the SceneFlow dataset, both of which are superior to GC-Net, DispNet, and PSMnet, and have higher reconstruction accuracy. The correlation between D and d is high and exhibits a positive correlation, while the correlation between D and dr is low. The geometric morphology expressions of small craters fitted by using D and d are significantly different from the expressions proposed by other scholars for large craters. This study provides a priori knowledge for the subsequent Von Karmen crater survey mission in the SPA Basin.
Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa
Authors: Rogers Grant , Koper Patrycja , Ruktanonchai Cori , Ruktanonchai Nick , Utazi Edson , Woods Dorothea , Cunningham Alexander , Tatem Andrew J. , Steele Jessica , Lai Shengjie , Sorichetta Alessandro
Published: August 2023

Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.
Consistency Analysis and Accuracy Evaluation of Multi-Source Land Cover Data Products in the Eastern European Plain
Authors: Jiang Guangmao , Wang Juanle , Li Kai , Xu Chen , Li Heng , Jin Zongyi , Liu Jingxuan
Published: August 2023

Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were analyzed and evaluated for component similarity, type confusion degree, spatial consistency, and accuracy verification. The research found that the three products provided consistent descriptions of land-cover types in the East European Plain. There was a strong correlation in the type area between the different products, with a correlation coefficient >0.85. Medium-to-high-consistency areas represented 92.31% of the total plains area. The low-consistency areas were mainly concentrated on Yuzhny Island, Kola Peninsula, and Pechora River Basin. The comparison revealed high consistency among the three products in identifying forest, cropland, water, and permanent ice/snow types. However, the consistency was poor for shrubs, wetlands, and bare land. Using the GLCVSS_V1 validation dataset, the highest overall accuracy among the assessed land cover data products was observed in the FROM_GLC (73.96%), followed by GlobeLand30 (69.80%) and GLC_FCS30 (67.29%). The FROM_GLC dataset is suitable for studying forests, tundra, water, and providing an overall representation of the region’s land cover. The GLC_FCS30 dataset is more suitable for agricultural research. The differences between products arise from the differences in classification systems, algorithms, and data correction. In the future, it will be necessary to utilize the advantages of different products for data fusion, focusing on areas with high heterogeneity and easily confused types, and improving the reliability of land-cover data products.
Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images
Authors: Zhang Jiaqing , Lei Jie , Xie Weiying , Li Daixun
Published: August 2023

The fusion of hyperspectral and LiDAR images plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, such as Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we propose an Invariant Attribute-driven Binary Bi-branch Classification (IABC) method, which is a unified network that combines a binary Convolutional Neural Network (CNN) and a GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structural representation of HSI and LiDAR images in the spectral–spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrate the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for the accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling the real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.
Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France
Authors: Corchia Timothée , Bonan Bertrand , Rodríguez-Fernández Nemesio , Colas Gabriel , Calvet Jean-Christophe
Published: August 2023

In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France’s global Land Data Assimilation System (LDAS-Monde) tool in order to jointly analyse soil moisture and leaf area index (LAI). For the first time, observation operators based on neural networks (NNs) are trained with ISBA simulations and LAI observations from the PROBA-V satellite to predict the ASCAT backscatter signal. The trained NN-based observation operators are implemented in LDAS-Monde, which allows the sequential assimilation of backscatter observations. The impact of the assimilation is evaluated over southwestern France. The simulated and analysed backscatter signal, surface soil moisture, and LAI are evaluated using satellite observations from ASCAT and PROBA-V as well as in situ soil moisture observations. An overall improvement in the variables is observed when comparing the analysis with the open-loop simulation. The impact of the assimilation is greater over agricultural areas.
The Spatiotemporal Distribution of NO2 in China Based on Refined 2DCNN-LSTM Model Retrieval and Factor Interpretability Analysis
Authors: Chen Ruming , Hu Jiashun , Song Zhihao , Wang Yixuan , Zhou Xingzhao , Zhao Lin , Chen Bin
Published: August 2023

With the advancement of urbanization in China, effective control of pollutant emissions and air quality have become important goals in current environmental management. Nitrogen dioxide (NO2), as a precursor of tropospheric ozone and fine particulate matter, plays a significant role in atmospheric chemistry research and air pollution control. However, the uneven ground monitoring stations and low temporal resolution of polar-orbiting satellites set challenges for accurately assessing near-surface NO2 concentrations. To address this issue, a spatiotemporal refined NO2 retrieval model was established for China using the geostationary satellite Himawari-8. The spatiotemporal characteristics of NO2 were analyzed and its contribution factors were explored. Firstly, seven Himawari-8 channels sensitive to NO2 were selected by using the forward feature selection based on information entropy. Subsequently, a 2DCNN-LSTM network model was constructed, incorporating the selected channels and meteorological variables as retrieval factors to estimate hourly NO2 in China from March 2018 to February 2020 (with a resolution of 0.05°, per hour). The performance evaluation demonstrates that the full-channel 2DCNN-LSTM model has good fitting capability and robustness (R2 = 0.74, RMSE = 10.93), and further improvements were achieved after channel selection (R2 = 0.87, RMSE = 6.84). The 10-fold cross-validation results indicate that the R2 between retrieval and measured values was above 0.85, the MAE was within 5.60, and the RMSE iwas within 7.90. R2 varied between 0.85 and 0.90, showing better validation at mid-day (R2 = 0.89) and in spring and fall transition seasons (R2 = 0.88 and R2 = 0.90). To investigate the cooperative effect of meteorological factors and other air pollutants on NO2, statistical methods (beta coefficients) were used to test the factor interpretability. Meteorological factors as well as other pollutants were analyzed. From a statistical perspective, PM2.5, boundary layer height, and O3 were found to have the largest impacts on near-surface NO2 concentrations, with each standard deviation change in these factors leading to 0.28, 0.24, and 0.23 in standard deviations of near-surface NO2, respectively. The findings of this study contribute to a comprehensive understanding of the spatiotemporal distribution of NO2 and provide a scientific basis for formulating targeted air pollution policies.
Landscape Ecological Risk Assessment and Analysis of Influencing Factors in Selenga River Basin
Authors: Li Wangping , Lin Qingrun , Hao Junming , Wu Xiaodong , Zhou Zhaoye , Lou Peiqing , Liu Yadong
Published: August 2023

Land degradation under the influence of global warming and ecological environmental destruction due to poor land management is the main challenge facing the Mongolian Plateau, and its future ecological risk change trends and drivers are also unclear. Therefore, to address the context relevant to this challenge, planning based on measured information from land use patterns is required. Based on land use and land cover (LULC), this study evaluates the landscape ecological risk (LER) of the Selenga River Basin by calculating the landscape pattern index. The spatiotemporal pattern and influencing factors of landscape ecological risk in the Selenga River Basin from 1990 to 2040 were analyzed. According to the results of LULC analysis, forest and grassland were the primary land use types in the Selenga River Basin. The built area, forest, and cropland showed an increasing trend, while the grassland area showed a fluctuating decreasing trend. From 1990 to 2010, the comprehensive land use dynamic degree showed a trend of rising first and then falling, specifically from 0.13% in 1990 to 0.29% in 2010, and will drop to 0.06% by 2040, indicating that the range of land use change is becoming more and more stable. The landscape ecological risk assessment shows a distribution pattern of “low at the edge and high in the middle”. The landscape ecological risk index (LER) first increases and then decreases, with the peak value in 2010 (0.085). By calculating the spatial aggregation of LER and the partial correlation with climate, we found that the Moran’s I index showed an “anti-V”-shaped change trend from 1990 to 2040, and the average landscape ecological risk presents positive spatial correlation, primarily with high-value aggregation, and peaked in 2010. Precipitation had a negative correlation with landscape ecological risk controlling for temperature, while there was a positive relationship between temperature and landscape ecological risk under the influence of controlling precipitation. This study provides a scientific basis for LULC planning in the Selenga River Basin, and is of great significance for maintaining the ecological security of the Mongolian Plateau.
Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets
Authors: Zayani Hayfa , Fouad Youssef , Michot Didier , Kassouk Zeineb , Baghdadi Nicolas , Vaudour Emmanuelle , Lili-Chabaane Zohra , Walter Christian
Published: August 2023

Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively).
Rapid Fog-Removal Strategies for Traffic Environments
Authors: Liu Xinchao , Hong Liang , Lin Yier
Published: August 2023

In a foggy traffic environment, the vision sensor signal of intelligent vehicles will be distorted, the outline of obstacles will become blurred, and the color information in the traffic road will be missing. To solve this problem, four ultra-fast defogging strategies in a traffic environment are proposed for the first time. Through experiments, it is found that the performance of Fast Defogging Strategy 3 is more suitable for fast defogging in a traffic environment. This strategy reduces the original foggy picture by 256 times via bilinear interpolation, and the defogging is processed via the dark channel prior algorithm. Then, the image after fog removal is processed via 4-time upsampling and Gaussian transform. Compared with the original dark channel prior algorithm, the image edge is clearer, and the color information is enhanced. The fast defogging strategy and the original dark channel prior algorithm can reduce the defogging time by 83.93–84.92%. Then, the image after fog removal is inputted into the YOLOv4, YOLOv5, YOLOv6, and YOLOv7 target detection algorithms for detection and verification. It is proven that the image after fog removal can effectively detect vehicles and pedestrians in a complex traffic environment. The experimental results show that the fast defogging strategy is suitable for fast defogging in a traffic environment.
Putting the Common Security and Defense Policy in the Eastern Mediterranean under Scrutiny: Türkiye’s Conflicting Role
Authors: Violakis Petros
Published: August 2023

The end of the Cold War and the emergence of a new global security and economic environment (Defence budgets’ decline, imminent threats from the Gulf War and NATO’s quest for a new identity), provided a window of opportunity for establishing and designing the EU’s security institution. Thus, what was initially considered farfetched after the 1992 Maastricht Treaty, entered a new phase, which included perception transformation, to be gradually incorporated into daily discourse. Hence, the 2003 “European Security Strategy”, the 2010 “Internal Security Strategy”, and later the 2016 “Global Strategy” reflect Member States’ perception of transformation and their belief that the EU needs a Common Security and Defence Strategy approach. Given these documents that indicate determination for notable integration and institutional restructuring, this analysis delves into the impact of Europeanisation concerning the implementation of institutional reforms and the CSDP. More specifically, this analysis probes the constraints of Europeanisation concerning the EU’s real operational dynamics, especially in light of the Eastern Mediterranean and Aegean Sea security challenges. These challenges are examined in relation to Türkiye’s revisionist public diplomacy, public policies, and the militarization of its foreign policy (including ongoing negotiations centered on migratory flows, assertions, and constructed narratives over Greek and Cypriot islands and seas). The situation has become more pronounced following the identification of gas and oil reserves in the area in 2010. This analysis has a two-fold focus: Firstly, to investigate Europeanisation impact on the operational implementation of CSDP (Common Security and Defence Policy) in time of crisis, and secondly, to evaluate the behavior of Türkiye within the Europeanisation framework, as proposed by Radaelli and Violakis.
Gender-Specific Patterns of Muscle Imbalance in Elite Badminton Players: A Comprehensive Exploration
Authors: Gasibat Qais , Abdullah Borhannudin , Samsudin Shamsulariffin , Alexe Dan Iulian , Alexe Cristina Ioana , Tohănean Dragoș Ioan
Published: August 2023

The high-intensity demands of overhead sports exert significant stress on the bilateral shoulder complex, triggering adaptive kinematics and a distinct strength imbalance between internal and external rotators. The imbalance being referred to in the given statement poses a potential risk for humeral head displacement and puts nearby tendons under tension, heightening the vulnerability to injury. This study aims to assess muscle imbalances in badminton athletes. The first hypothesis (H1) suggests that there are differences in internal and external shoulder rotation movements between dominant and non-dominant segments in badminton players. The second hypothesis (H2) proposes that there are variations in muscle imbalances based on gender among elite badminton players. The objectives are to analyze these differences and explore potential gender-related variations in muscle imbalances. The study seeks to contribute to the understanding of muscle imbalances in badminton athletes and potentially guide training and injury prevention strategies in the sport. Using a cutting-edge Hand-Held Dynamometer (HHD), a cohort of 30 elite badminton players underwent an assessment to uncover any bilateral shoulder rotation strength imbalances during a challenging five second isometric maximum contraction. The participants boasted an average age of 17.4 years and a mean playing experience of 7.23 years. The study revealed a notable difference in the ratio of external and internal strength between the dominant and non-dominant shoulders (p = 0.000). This discrepancy amounted to a striking 27.93% muscle imbalance in external rotation/internal rotation strength ratios, favoring the dominant shoulder. Moreover, gender-specific differences were detected, with male players exhibiting a 24.54% muscle imbalance in favor of the dominant shoulder, while female players showcased a more substantial 31.33% imbalance (p = 0.000). In light of these findings, it became evident that elite badminton players possess considerably stronger dominant shoulders compared with their non-dominant counterparts. Furthermore, the study revealed that male players experience less muscular imbalance than their female counterparts.
European Union 2030 Carbon Emission Target: The Case of Turkey
Authors: Kayakuş Mehmet , Terzioğlu Mustafa , Erdoğan Dilşad , Zetter Selin Aygen , Kabas Onder , Moiceanu Georgiana
Published: August 2023

Climate awareness caused by the threat of global warming is the number one agenda item for developed and developing economies. Plans developed in this context, environmentally friendly trends in economic activities, and countries’ efforts to adapt to sustainable development have enabled new road maps. The most important of these efforts is the Paris Climate Agreement signed in 2015 and the Green Deal implemented by the European Union (EU) within the framework of this agreement. In this study, the carbon emissions of Turkey, which has important trade relations with the EU, were estimated using machine learning techniques, and a prediction was made for 2030 based on the results obtained. These results were evaluated in line with the targets of the Green Deal. The R2 of support vector regression (SVR), which was applied in the model as one of the machine learning techniques, was found to be 98.4%, and it was found to have the highest predictive power. This technique was followed by multiple linear regression (MLR) with a 97.6% success rate and artificial neural networks (ANN) with a 95.8% success rate, respectively. According to the estimates achieved with the most successful model, SVR, Turkey’s carbon emissions are expected to be 723.97 million metric tons (mt) of carbon dioxide (CO2) in 2030, the target year set by the EU. This level is 42% higher than the target that needs to be achieved given the data existing in 2019. According to these results, Turkey will not be able to reach the targets set by the EU unless it increases its coal-based energy consumption and provides incentives for renewable energy sources.
Renewable Energy Generation Technologies for Decarbonizing Urban Vertical Buildings: A Path towards Net Zero
Authors: de Oliveira Raphael Souza , de Oliveira Meire Jane Lima , Nascimento Erick Giovani Sperandio , Sampaio Renelson , Nascimento Filho Aloísio Santos , Saba Hugo
Published: August 2023

Greenhouse gas (GHG) emissions, especially CO2, represent a global concern. Among those responsible for CO2 emissions, buildings stand out due to the consumption of energy from fossil fuels. In this sense, initiatives for the decarbonization of buildings and construction tends to contribute to the achievement of the target defined in the Paris Agreement of limiting the increase in global temperature to 1.5 degrees Celsius above pre-industrial levels, as well as in achieving the Sustainable Development Goals (SDG) and of the Triple Bottom Line (TBL). This article aimed to identify renewable energy generation technologies that can be applied in urban vertical constructions, contributing to the reduction of carbon emissions in the atmosphere. To this end, the following methodology was adopted: a survey of the Conferences of the Parties on climate change; identification of European Union Legislative Directives for the decarbonization of buildings; and a literature review to identify research that deals with renewable energy generation technologies that can be adopted in buildings. The results indicated that there seems to be a correlation between the growth in the number of articles that deal with the topic of decarbonizing buildings and the increase in world concerns about global warming. A hybrid microgrid proposal, combining different sources of renewable energy such as solar photovoltaic, wind, biomass, micro-hydroelectric, and others for vertical buildings with more than five floors, is presented as viable to achieve zero emissions in these buildings, contributing to future research, that can carry out quantitative analyses and feasibility studies, as well as for experiments and applications in existing buildings and the projects of new vertical constructions.
Towards Evaluating the Robustness of the Open-Source Product Community under Multiple Attack Strategies
Authors: Zhou Hongli , Yang Mingxuan
Published: August 2023

As an emerging product innovation model, open-source innovation has undergone rapid development in recent years. The sustainability and stability of the open-source product community (OSPC) is crucial for product innovation, and the effect that users have on the OSPC’s robustness is an important and closely scrutinized topic. This paper explores the robustness of the OSPC from the aspect of user knowledge contribution. We first construct an OSPC network and analyze its characteristics. An improved node evaluation method is then proposed to identify different types of knowledge contribution nodes. Further, seven node- and edge-based attack strategies are designed to simulate network robustness changes, with evaluation indicators being proposed for structural robustness and knowledge robustness. The results reveal that our proposed node evaluation method can effectively identify nodes of different knowledge contribution types. Additionally, the network is found to have different robustness performance when facing multiple deliberate attacks on three important knowledge contribution node types. Moreover, the network shows different robustness characteristics when facing deliberate attacks on betweenness and weight edges. Our findings can benefit product innovation and OSPC managers by enhancing the robustness of the OSPC network.