bluuebunny/crossref_metadata_2025 huggingface dataset:
Overcoming the Imperialist Seduction: A Polylogue Reading of Mary towards a Theology of Peace
Authors: Quast-Neulinger Michaela
Published: September 2023

The current war waged by Russia against Ukraine once again sheds light on the ambivalent role of religion in violent conflicts, and especially the use of religious figures for imperialist political strategies. In this context, Mary is of particular interest, as she serves in Christian history (and presence) as the Queen of Peace, but could also take on characteristics of a warrior goddess. I suggest that investigating the complex use and abuse of Mary for imperialist strategies in the context of Christian–Muslim encounters can give some hints for overcoming the ever-present seduction of theopolitical imperialism, especially with regard to the current developments in parts of (Orthodox) Christian theology. Thus, I will first outline the premises of a political theology dedicated to peace that can only be worked out in a permanent polylogue of religions. Second, a historical overview shows the deep ambivalence of Mary as a theopolitical figure, particularly in the context of Christian–Muslim encounters. Thus, it is even more surprising that, thirdly, a close comparative reading of the Qur’anic Maryam can help to overcome the imperial theopolitical seduction. Finally, I will draw some conclusions about a theology of peace with particular regard to our conflictive times.
FSSBP: Fast Spatial–Spectral Back Projection Based on Pan-Sharpening Iterative Optimization
Authors: Tao Jingzhe , Ni Weihan , Song Chuanming , Wang Xianghai
Published: September 2023

Pan-sharpening is an important means to improve the spatial resolution of multispectral (MS) images. Although a large number of pan-sharpening methods have been developed, improving the spatial resolution of MS while effectively maintaining its spectral information has not been well solved so far, and it has also been taken as a criterion to measure whether the sharpened product can meet the practical needs. The back-projection (BP) method iteratively injects spectral information backwards into the sharpened results in a post-processing manner, which can effectively improve the generally unsatisfied spectral consistency problem in pan-sharpening methods. Although BP has received some attention in recent years in pan-sharpening research, the existing related work is basically limited to the direct utilization of the BP process and lacks a more in-depth intrinsic integration with pan-sharpening. In this paper, we analyze the current problems of improving the spectral consistency based on BP in pan-sharpening, and the main innovative works carried out on this basis include the following: (1) We introduce the spatial consistency condition and propose the spatial–spectral BP (SSBP) method, which takes into account both spatial and spectral consistency conditions, to improve the spectral quality while effectively solving the problem of spatial distortion in the results. (2) The proposed SSBP method is analyzed theoretically, and the convergence condition of SSBP and a more relaxed convergence condition for a specific BP type, degradation transpose BP, are given and proved theoretically. (3) Fast computation of BP and SSBP is investigated, and non-iterative fast BP (FBP) and fast SSBP algorithms (FSSBP) methods are given in a closed-form solution with significant improvement in computational efficiency. Experimental comparisons with combinations formed by seven different BP-related post-processing methods and up to 18 typical base methods show that the proposed methods are generally applicable to the optimization of the spatial–spectral quality of various sharpening methods. The fast method improves the computational speed by at least 27.5 times compared to the iterative version while maintaining the evaluation metrics well.
Analysis of Land Surface Temperature Sensitivity to Vegetation in China
Authors: Qian Zhonghua , Sun Yingxiao , Chen Zheng , Ji Fei , Feng Guolin , Ma Qianrong
Published: September 2023

China has emerged as one of the global leaders in greening, achieved through human land use management practices, particularly afforestation projects. However, accurately calculating the energy balance processes of vegetated areas remains challenging because of the complexity of physical mechanisms, parameterization schemes, and driving dataset used in current research. In this study, we address these challenges by employing moving window methods in space inspired by “space-for-time”. This approach allows us to eliminate the influence of climate signals on vegetation development over long periods and determine the sensitivity of seasonal contributions of Land Surface Temperature (LST) to Leaf Area Index (LAI) in China from 2001 to 2018. Our findings reveal that the sensitivity of LST to LAI in the climatology period is approximately −0.085 K·m2·m−2, indicating a cooling effect. Moreover, the climatological trend remains negative, suggesting that Chinese vegetation greening is playing an increasingly important role in cooling the land surface. Considering the energy balance equation, we further investigate the underlying mechanisms. It is observed that the radiative feedback consistently contributes positively, while the non-radiative feedback always exerts a negative influence on the sensitivity. These results provide valuable insights into the complex interactions between vegetation greening and land surface temperature in China, providing informed land management and climate adaptation strategies. Understanding these trends and mechanisms is essential for sustainable and effective environmental planning and decision making.
Impact of Deep Convolutional Neural Network Structure on Photovoltaic Array Extraction from High Spatial Resolution Remote Sensing Images
Authors: Li Liang , Lu Ning , Jiang Hou , Qin Jun
Published: September 2023

Accurate information on the location, shape, and size of photovoltaic (PV) arrays is essential for optimal power system planning and energy system development. In this study, we explore the potential of deep convolutional neural networks (DCNNs) for extracting PV arrays from high spatial resolution remote sensing (HSRRS) images. While previous research has mainly focused on the application of DCNNs, little attention has been paid to investigating the influence of different DCNN structures on the accuracy of PV array extraction. To address this gap, we compare the performance of seven popular DCNNs—AlexNet, VGG16, ResNet50, ResNeXt50, Xception, DenseNet121, and EfficientNetB6—based on a PV array dataset containing 2072 images of 1024 × 1024 size. We evaluate their intersection over union (IoU) values and highlight four DCNNs (EfficientNetB6, Xception, ResNeXt50, and VGG16) that consistently achieve IoU values above 94%. Furthermore, through analyzing the difference in the structure and features of these four DCNNs, we identify structural factors that contribute to the extraction of low-level spatial features (LFs) and high-level semantic features (HFs) of PV arrays. We find that the first feature extraction block without downsampling enhances the LFs’ extraction capability of the DCNNs, resulting in an increase in IoU values of approximately 0.25%. In addition, the use of separable convolution and attention mechanisms plays a crucial role in improving the HFs’ extraction, resulting in a 0.7% and 0.4% increase in IoU values, respectively. Overall, our study provides valuable insights into the impact of DCNN structures on the extraction of PV arrays from HSRRS images. These findings have significant implications for the selection of appropriate DCNNs and the design of robust DCNNs tailored for the accurate and efficient extraction of PV arrays.
Driving Source of Change for Ionosphere before Large Earthquake -Vertical Ground Motion-
Authors: Chen Chia-Hung , Oyama Koichiro , Jhuang Hau-Kun , Das Uma
Published: September 2023

This paper discusses the relationship between the vertical ground motion and ionospheric disturbances before the Kumamoto earthquake on 16 April 2016, in Kyushu, Japan, using the vertical ground motion measured by slant gauges widely distributed in Kyushu, and the NmF2 observed by ionosondes in Japan and another region. We provide evidence that vertical ground motion excites internal gravity waves (IGWs) that disturb changes in the ionospheric plasma density. From the spectral analysis results of the vertical ground motion data, the summation of various period (frequency) components analyzed from the original data of the slant gauge shows a possible correlation with the change of NmF2 before the earthquake. On the other hand, the influence of the geomagnetic disturbance on vertical ground motion seems to exist. However, we cannot confirm that vertical ground motion is influenced by the geomagnetic disturbance (Kp index) and that the earthquake is triggered by the geomagnetic disturbance. There are two conditions for the vertical ground motion to disturb variations in the ionospheric plasma density: (1) The effective vertical ground motion period should be shorter than 5 h. In addition, (2) vertical ground motion should continue to exist so that wave energy can be continuously injected into the atmosphere. A possible mechanism with which to modify the ionosphere is discussed. The results of this study can provide a basis for the future ionospheric precursors of earthquakes by using the vertical ground motion.
Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images
Authors: Liu Zhigang , Luo Shengjie , Wang Yiting
Published: September 2023

Because of the counterintuitive imaging and confusing interpretation dilemma in Synthetic Aperture Radar (SAR) images, the application of deep learning in the detection of SAR targets has been primarily limited to large objects in simple backgrounds, such as ships and airplanes, with much less popularity in detecting SAR vehicles. The complexities of SAR imaging make it difficult to distinguish small vehicles from the background clutter, creating a barrier to data interpretation and the development of Automatic Target Recognition (ATR) in SAR vehicles. The scarcity of datasets has inhibited progress in SAR vehicle detection in the data-driven era. To address this, we introduce a new synthetic dataset called Mix MSTAR, which mixes target chips and clutter backgrounds with original radar data at the pixel level. Mix MSTAR contains 5392 objects of 20 fine-grained categories in 100 high-resolution images, predominantly 1478 × 1784 pixels. The dataset includes various landscapes such as woods, grasslands, urban buildings, lakes, and tightly arranged vehicles, each labeled with an Oriented Bounding Box (OBB). Notably, Mix MSTAR presents fine-grained object detection challenges by using the Extended Operating Condition (EOC) as a basis for dividing the dataset. Furthermore, we evaluate nine benchmark rotated detectors on Mix MSTAR and demonstrate the fidelity and effectiveness of the synthetic dataset. To the best of our knowledge, Mix MSTAR represents the first public multi-class SAR vehicle dataset designed for rotated object detection in large-scale scenes with complex backgrounds.
An Improved S2A-Net Algorithm for Ship Object Detection in Optical Remote Sensing Images
Authors: Li Jianfeng , Chen Mingxu , Hou Siyuan , Wang Yongling , Luo Qinghua , Wang Chenxu
Published: September 2023

Ship detection based on remote sensing images holds significant importance in both military and economic domains. Ships within such images exhibit diverse scales, dense distributions, arbitrary orientations, and narrow shapes, which pose challenges for accurate recognition. This paper introduces an improved S2A-Net (Single-shot Alignment Network) based oriented object detection algorithm for ship detection. In network structure, pyramid squeeze attention is embedded in order to focus on key features and a context information module is designed to enhance the context understanding capability of the network. In the training strategy, considering the distortion problems such as blurring and low contrast in remote sensing images, a fog density and depth decomposition-based unpaired image dehazing network D4 is adopted to improve the image quality, besides, an image weight sampling strategy is proposed to enhance the training opportunities of small and difficult samples, thereby mitigating the issue of imbalanced ship category distribution. Experimental results demonstrate that the improved S2A-Net algorithm achieves the mean average precision of 77.27% for ship detection in the FAIR1M dataset, which is 5.6% better than the original S2A-Net algorithm, and outperforms the current common object detection algorithms.
Study on the Polarization Pattern Induced by Wavy Water Surfaces
Authors: Cheng Haoyuan , Zhang Qianli , Wan Zhenhua , Zhang Zhongyuan , Qin Jin
Published: September 2023

In nature, the wavy ocean surface is a common polarizer, which can change the polarization state of incident light by refraction and reflection and form a new polarization pattern different from the atmosphere. In this paper, we establish the polarized optical transmission model of wavy ocean surface reflection and refraction and simulate polarization patterns induced by wavy ocean surfaces. We study the polarization patterns reflected by wavy water surfaces and polarization patterns inside and outside Snell’s window under wavy ocean surfaces. The correctness of the simulation results is verified by qualitative and quantitative analysis. The environmental factors affecting the corresponding polarization patterns are discussed. Through contrastive analysis, we find that polarization patterns induced by wavy water surfaces are predictable and regular, which has great potential for human application. This kind of polarization pattern is influenced by the sun’s position and water surface condition. The study will promote the development of remote sensing, target detection, and polarization navigation.
An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector
Authors: Reis Nuno , Machado da Silva José , Correia Miguel Velhote
Published: September 2023

The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm’s confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.
Enhancing Spatial Resolution of GNSS-R Soil Moisture Retrieval through XGBoost Algorithm-Based Downscaling Approach: A Case Study in the Southern United States
Authors: Luo Qidi , Liang Yueji , Guo Yue , Liang Xingyong , Ren Chao , Yue Weiting , Zhu Binglin , Jiang Xueyu
Published: September 2023

The retrieval of soil moisture (SM) using the Global Navigation Satellite System-Reflectometry (GNSS-R) technique has become a prominent topic in recent years. Although prior research has reached a spatial resolution of up to 9 km through the Cyclone Global Navigation Satellite System (CYGNSS), it is insufficient to meet the requirements of higher spatial resolutions for hydrological or agricultural applications. In this paper, we present an SM downscaling method that fuses CYGNSS and SMAP SM. This method aims to construct a dataset of CYGNSS observables, auxiliary variables, and SMAP SM (36 km) products. It then establishes their nonlinear relationship at the same scale and finally builds a downscale retrieval model of SM using the eXtreme Gradient Boosting (XGBoost) algorithm. Focusing on the southern United States, the results indicate that the SM downscaling method exhibits robust performance during both the training and testing processes, enabling the generation of a CYGNSS SM product with a 1 day/3 km resolution. Compared to existing methods, the spatial resolution is increased threefold. Furthermore, in situ sites are utilized to validate the downscaled SM, and spatial correlation analysis is conducted using MODIS EVI and MODIS ET products. The CYGNSS SM obtained by the downscaling model exhibits favorable correlations. The high temporal and spatial resolution characteristics of GNSS-R are fully leveraged through the downscaled method proposed. Furthermore, this work provides a new perspective for enhancing the spatial resolution of SM retrieval using the GNSS-R technique.
Effects of Habitat Change on the Wintering Waterbird Community in China’s Largest Freshwater Lake
Authors: Duan Houlang , Pan Yiwen , Yu Xiubo , Xia Shaoxia
Published: September 2023

Poyang Lake wetland in the middle and lower Yangtze River floodplain provides important wintering sites for migratory waterbirds. Extreme climatic events and human activities have resulted in the degradation and redistribution of habitat over the last few decades. However, the effects of habitat changes on the abundance of waterbirds remain unclear. We used long-term waterbird monitoring data and Landsat remote-sensing data to characterize changes in abundance and the relationship between habitat variation and abundance. A total of 113 waterbird species were recorded in the wintering period between 1999 and 2021, including 23 globally threatened species. Greater White-fronted Goose (Anser albifrons), Swan Goose (Anser cygnoides), Bean Goose (Anser fabalis), and Tundra Swan (Cygnus columbianus) were the dominant species. A total of 46 species with more than 6 annual surveys and average abundance >100 were recorded between 1999 and 2021. For most species, the mean abundance across all sub-lakes was higher in the first year than in the last year, and no obvious changes were observed over the last 20 years. The mean abundances of the vulnerable species Hooded Crane (Grus monacha) and White-naped Crane (Grus vipio) significantly declined between 1999 and 2021. No significant changes in the mean abundance of all foraging groups were observed. The area of water bodies increased, and the area of mudflats decreased. For most species with significant changes in abundance, habitat change did not greatly contribute to variation in mean abundance. The reduction in the area of mudflats greatly contributed to declines in the mean abundance of the gray heron (Ardea cinerea) and gadwall (Anas strepera).
A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data
Authors: Liu Bin , Yang Xiaomei , Wang Zhihua , Ding Yaxin , Zhang Junyao , Meng Dan
Published: September 2023

Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes particularly conspicuous in regions characterized by significant deforestation, like Southeast Asia, where forest mapping uncertainty is more pronounced, presenting users with challenges in selecting appropriate datasets across diverse regions. Moreover, this situation impedes the further enhancement of accuracy for forest mapping products. The aim of this research is to assess the consistency and accuracy of six recently produced forest mapping products in Southeast Asia. These products include three 10 m land cover products (Finer Resolution Observation and Monitoring Global LC (FROM-GLC10), ESA WorldCover 10 m 2020 (ESA2020), and ESRI 2020 Land Cover (ESRI2020)) and three forest thematic mapping products (Global PALSAR-2 Forest/Non-Forest map (JAXA FNF2020), global 30 m spatial distribution of forest cover in 2020 (GFC30_2020), and Generated_Hansen2020, which was synthesized based on Hansen TreeCover2010 (Hansen2010) and Hansen Global Forest Change (Hansen GFC) for the year 2020). Firstly, the research compared the area and spatial consistency. Next, accuracy was assessed using field validation points and manual densification points. Finally, the research analyzed the geographical environmental and biophysical factors influencing consistency. The results show that ESRI2020 had the highest overall accuracy for forest, followed by ESA2020, FROM-GLC10, and Generated_Hansen2020. Regions with elevations ranging from 200 to 3000 m and slopes below 15° or above 25° showed high spatial consistency, whereas other regions showed low consistency. Inconsistent regions showed complex landscapes heavily influenced by human activities; these regions are prone to being confused with shrubs and cropland and are also impacted by rubber and oil palm plantations, significantly affecting the accuracy of forest mapping. Based on the research findings, ESRI2020 is recommended for mountainous areas and abundant forest regions. However, in areas significantly affected by human activities, such as forest and non-forest edges and mixed areas of plantations and natural forests, caution should be taken with product selection. The research has identified areas of forest inconsistency that require attention in future forest mapping. To enhance our understanding of forest mapping and generate high-precision forest cover maps, it is recommended to incorporate multi-source data, subdivide forest types, and increase the number of sample points.
Editorial for the Topic “A Themed Issue in Memory of Academician Duzheng Ye (1916–2013)”
Authors: Zou Xiaolei , Cai Ming , Wu Guoxiong , Tan Zhemin
Published: September 2023

This Topic covers a wide range of topics, including atmospheric dynamics and physics, synoptic weather, climate variability, climate change, and remote sensing observations for weather and climate studies [...]
Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference
Authors: Wu Xiaojun , Zhou Yibo , Wu Daolong , Xiao Haitao , Lu Yaya , Li Hanbing
Published: September 2023

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%.
The Simultaneous Determination of Nine Furocoumarins in Angelica dahurica Using UPLC Combined with the QAMS Approach and Novel Health Risk Assessment Based on the Toxic Equivalency Factor
Authors: Wang Zhao , Zan Ke , Hu Xiao-Wen , Kang Shuai , Li Hai-Liang , Zuo Tian-Tian , Jin Hong-Yu , Ma Shuang-Cheng
Published: September 2023

Objective: This study aimed to provide data for the type and content of linear furocoumarins (FCs) in Angelica dahurica (AD) in order to assess their cumulative risks and provide a scientific basis for the rational use and quality evaluation of the medicinal AD to improve public health. Methods: A UPLC method was developed for the simultaneous determination of nine FCs initially by using imperatorin (Im) as the internal standard substance, including Im, phellopterin (Ph), isoimperatorin (Is), oxypeucedanin hydrate (Oh), byakangelicin (Bn), xanthotoxin (8-MOP), bergapten (5-MOP), byakangelicol (Bl), and oxypeucedanin (Op) in two species of Angelica dahurica (AD). And, the risk assessment for the total FCs in AD was explored using the hazard index combined with the toxic equivalency factor (TEF-HI) strategy for the first time. Results: The established method revealed acceptable applicability, and there were no significant differences compared with the external standard method (ESM). The quantitative results demonstrated that the total content of FCs in Angelica dahurica (BZ) were higher than that in Angelica dahurica var. formosana (HBZ), and there was a great difference between the Bl and Op. Moreover, the risk assessment data revealed that the risk of total FCs in AD to human health was low. Conclusions: The established UPLC method that determined nine FCs in AD using a single marker could solve the problem of difficulty in obtaining a chemical reference substance with high purity and requiring a long determination time. And, the TEF-HI risk assessment approach associated with FCs in ADs could guide the rational utilization of toxic FCs in ADs in the progress of improving public health safety. In short, the whole systematic strategy provides a scientific basis for rational quality evaluation and the healthy use of related herbal medicines.
Extraction of Gallium from the Brown Corundum Dust with a One-Step Alkaline Leaching Process
Authors: Zhang Juhua , Gao Cong , Hui Xujie , Chang Yuwei
Published: September 2023

Brown corundum dust, which is created during the manufacturing of brown corundum using bauxite as the raw material, is a vital carrier of gallium. To ascertain the presence of the contained gallium, the brown corundum dust was measured and characterized (XRF, XRD, ICP-OES, EPMA, SEM-EDS, etc.). Gallium was extracted from the brown corundum dust using a one-step alkali leaching process, and thermodynamic calculations were utilized to assess the viability of the leaching reactions. The effects of leaching parameters (NaOH solution concentration, leaching time, leaching temperature, solid–liquid ratio and stirring speed) on the recovery of gallium during the leaching process were investigated. A gallium recovery of 96.83% was discovered to be possible with the following parameters: 200 g/L of NaOH, 363 K for the leaching temperature, 60 min for the leaching time, 1:10 g/mL for the solid–liquid ratio, and 850 rpm for the stirring rate. Gallium extraction was negatively impacted by raising the leaching temperature above 363 K and the concentration of NaOH solution above 200 g/L due to the accelerated side reactions between Na+, K+, SiO44− and AlO2−, which led to the precipitation of aluminosilicates that absorbed gallium from the solution. The influences of leaching parameters such as the temperature, NaOH solution concentration, and solid–liquid ratio on the leaching kinetics were examined. It was demonstrated that the leaching process followed the unreacted shrinking core model, that the interfacial diffusion associated with the contacting surface area served as the controlling step, and that the apparent activation energy was 42.83 kJ/mol. It turned out that the final kinetic equation was 1/(1 − α)1/3 − 1 = 4.34 × 104 × (CNaOH)2.12 (L/S)0.43exp[−42835/(RT)] t.
Photocatalytic Removal of Crystal Violet Dye Utilizing Greenly Synthesized Iron Oxide Nanoparticles
Authors: Yassin Mohamed Taha , Al-Otibi Fatimah O. , Al-Askar Abdulaziz A.
Published: September 2023

The presence of synthetic industrial dyes in the environment poses significant risks to aquatic ecosystems, human health, and economies. This study aims to synthesize iron oxide nanoparticles (IONPs) using a green method, analyze them using physicochemical techniques, and examine the effectiveness with which they photocatalytically degrade crystal violet dye in sunlight. Fourier transform infrared spectroscopy (FTIR) analysis revealed that the biogenic IONPs showed a UV peak at a wavelength of 241 nm, with functional groups including phenols, alkynes, and alkenes. X-ray diffraction (XRD) analysis confirmed the amorphous nature of the bioinspired IONPs. The mean diameter of the biogenic IONPs was 49.63 ± 9.23 nm, and they had a surface charge of −5.69 mV. The efficiency with which the synthesized IONPs removed the crystal violet dye was evaluated under dark and sunlight conditions. The removal efficiency was found to be concentration and time dependent, with a peak removal percentage of 99.23% being achieved when the IONPs were exposed to sunlight for 210 min. The biogenic IONPs also demonstrated antioxidant activity, with a relative IC50 value of 64.31 µg/mL. In conclusion, biogenic IONPs offer a viable and environmentally friendly approach for eradicating industrial synthetic dyes and remediating contaminated environments and aquatic ecosystems.
Successful and Emerging Cyberbullying Prevention Programs: A Narrative Review of Seventeen Interventions Applied Worldwide
Authors: Siddiqui Sohni , Schultze-Krumbholz Anja
Published: September 2023

The advent of the internet has channeled more online-related tasks into our lives and they have become a pre-requisite. One of the concerns with high internet usage is the multiplication of cyber-associated risky behaviors such as cyber aggression and/or cyberbullying. Cyberbullying is an emerging issue that needs immediate attention from many stakeholders. The aim of this study is to review existing successful and emerging interventions designed to prevent cyberbullying by engaging individuals through teacher professional development and adopting a whole-school approach. The review presents the strengths and limitations of the programs and suggestions to improve existing interventions. Preparing interventions with a strong theoretical framework, integrating the application of theories in interventions, promoting proactive and reactive strategies in combination, beginning with baseline needs assessment surveys, reducing time on digital devices and the digital divide among parents and children, promoting the concepts of lead trainer, peer trainer, and hot spots, focusing on physical activity, and use of landmarks are some of the recommendations proposed by the authors. In addition to face-to-face intervention sessions, it is suggested to update existing intervention programs with games and apps and to evaluate this combination.
A Theory of Dynamical Responses for Metal Films: Surface Roughness Effects
Authors: Praill Sam , Lawton Charlotte , Balable Hasan , Deng Hai-Yao
Published: September 2023

A generic expression is derived for the dynamical response function of metal films, with conductivity tensors as the only input. The semi-classical model is then used to provide an analytical expression for the conductivity tensor, thus establishing a kinetic theory for the response function. A major advantage of the theory is its ability to handle surface roughness effects through the use of the so-called specularity parameter. We applied the theory to study the properties of surface plasma waves. It is found that surface roughness does not affect the dispersion, but rather the decay rate of these waves. Furthermore, it significantly affects the spectral weight carried by the SPW resonances, which diminishes toward zero as the specularity parameter approaches unity.
Exploring Teacher’s Professional Identity in Relationship to Leadership: A Latent Profile Analysis
Authors: Bolat Özgür , Toytok Esef Hakan
Published: September 2023

As a result of the COVID-19 outbreak, which has affected the whole world, teacher leadership has gained more significance. Teacher leadership is a multidimensional concept. While some teachers exercise leadership only at the classroom level, others exercise leadership at a macro level. This level of leadership enactment is directly related to how teachers construct their professional identity in relation to leadership. This study aims to explore the leadership-related professional identities of teachers who came to the fore, especially during the COVID-19 pandemic period by using latent profile analysis. The data were collected from a sample of 710 teachers. Teachers completed the Teacher Leadership Behavior Scale, which measures teacher leadership behaviors at four levels: classroom, parental, micro-level, and macro-level. Participants also completed the Rosenberg self-esteem scale, the teacher self-efficacy scale, the openness to authority scale, and the job satisfaction scale. As a result of latent profile analysis, three distinct profiles of teacher leadership identity emerged: those who exercise leadership at a minimum level, an intermediate level, and a maximum level across all four levels of teacher leadership. These profiles were labeled as “restricted professionality”, “intermediate professionality”, and “extended professionality”, drawing upon Hoyle’s conceptualization of teacher professional identity. These findings suggest that teachers construct their identities differently regarding teacher leadership. It is essential for school leaders to first explore the different levels of professional identity and tailor their professional development activities accordingly.
Investigation of a SWAT Model for Environmental Health Management Based on the Water Quality Parameters of a Stream System in Central Anatolia (Türkiye)
Authors: Germeç Eren , Ürker Okan
Published: September 2023

Water is one of the most critical factors affecting environmental health. Therefore, it is essential to be able to predict water behavior in nature and prevent water pollution to avoid environmental health problems. In order to predict the behavior of water, the hydrological cycle needs to be evaluated at the basin level. To this aim, hydrological models can be used to obtain mathematical representations of hydrological processes. These models allow the anticipation and monitoring of issues regarding water quality, pollution, sediment transport, and proliferation of oil, and petroleum derivatives, among others, which can affect environmental health. In this study, a 2D surface water model was created using the soil and water assessment tool (SWAT) to simulate the lotic ecosystem and present water quality in the Tatlıçay Basin and to propose solutions for improving environmental health in the Cankiri provincial center in Türkiye. The accuracy of the input data and the validity of the model were tested with calibration and validation studies by using monthly or trimonthly observation data obtained from the flow observation and water quality stations of the General Directorate of State Hydraulic Works from 2016 to 2020. The aim was to create a model able to provide fast, accurate, and practical solutions in the face of water-related and environmental issues. The calibration and validation of this model were successfully carried out with very few observation data. Since surface water models are dynamic, long-term daily or monthly flow and water quality measurements should increase the accuracy of their predictions. Additionally, in the presence of pollution sources that may affect environmental health, monitoring and analyses of their possible effects should be carried out. As one of the few studies from the Middle East describing a hydrological model, this research makes a significant contribution to the literature on environmental health.
Stochastic Dynamics Analysis of Epidemic Models Considering Negative Feedback of Information
Authors: Wu Wanqin , Luo Wenhui , Chen Hui , Zhao Yun
Published: September 2023

In this article, we mainly consider the dynamic analysis of a stochastic infectious disease model with negative feedback, a symmetric and compatible distribution family. Based on the sir epidemic model taking into account the isolation (y) and the death (v), we consider adding a new variable (w) to control the information of non-drug interventions, which measures transformations in isolation performance that determine the epidemic, and establish a new model. We have demonstrated various properties of the model solution using Lyapunov functions for this model. To begin with, we demonstrate the existence and uniqueness of the global positive solution. After that, we obtained the conditions that need to be met for the extinction of the disease and verified the correctness of the conclusion by simulating numerical values. Afterwards, we prove the stochastic boundedness and stationary distribution of the model solution.
A Topological Approach to the Bézout’ Theorem and Its Forms
Authors: Bagchi Susmit
Published: September 2023

The interplays between topology and algebraic geometry present a set of interesting properties. In this paper, we comprehensively revisit the Bézout theorem in terms of topology, and we present a topological proof of the theorem considering n-dimensional space. We show the role of topology in understanding the complete and finite intersections of algebraic curves within a topological space. Moreover, we introduce the concept of symmetrically complex translations of roots in a zero-set of a real algebraic curve, which is called a fundamental polynomial, and we show that the resulting complex algebraic curve is additively decomposable into multiple components with varying degrees in a sequence. Interestingly, the symmetrically complex translations of roots in a zero-set of a fundamental polynomial result in the formation of isomorphic topological manifolds if one of the complex translations is kept fixed, and it induces repeated real roots in the fundamental polynomial as a component. A set of numerically simulated examples is included in the paper to illustrate the resulting manifold structures and the associated properties.
Approximate Calculation of Functional Integrals Generated by Nonrelativistic and Relativistic Hamiltonians
Authors: Ayryan Edik , Hnatic Michal , Honkonen Juha , Malyutin Victor
Published: September 2023

The discussion revolves around the most recent outcomes in the realm of approximating functional integrals through calculations. Review of works devoted to the application of functional integrals in quantum mechanics and quantum field theory, nuclear physics and in other areas is presented. Methods obtained by the authors for approximate calculation of functional integrals generated by nonrelativistic Hamiltonians are given. One of the methods is based on the expansion in eigenfunctions of the Hamiltonian. In an alternate approach, the functional integrals are tackled using the semiclassical approximation. Methods for approximate evaluation of functional integrals generated by relativistic Hamiltonians are presented. These are the methods using functional polynomial approximation (analogue of formulas of a given degree of accuracy) and methods based on the expansion in eigenfunctions of the Hamiltonian, generating a functional integral.
Considerations Concerning the Little Group
Authors: Erler Jens
Published: September 2023

I very briefly review both the historical and constructive approaches to relativistic quantum mechanics and relativistic quantum field theory, including remarks on the possibility of a non-vanishing photon mass, as well as a foolhardy speculation regarding dark matter.
A Whole-Genome Analysis of the African Swine Fever Virus That Circulated during the First Outbreak in Vietnam in 2019 and Subsequently in 2022
Authors: Le Van Phan , Ahn Min-Ju , Kim Jun-Seob , Jung Min-Chul , Yoon Sun-Woo , Trinh Thi Bich Ngoc , Le Thi Ngoc , Kim Hye Kwon , Kang Jung-Ah , Lim Jong-Woo , Yeom Minjoo , Na Woonsung , Xie Xing , Feng Zhixin , Song Daesub , Jeong Dae Gwin
Published: September 2023

Since its initial report in Vietnam in early 2019, the African swine fever (ASF), a highly lethal and severe viral swine disease worldwide, continues to cause outbreaks in other Southeast Asian countries. This study analyzed and compared the genomic sequences of ASF viruses (ASFVs) during the first outbreak in Hung Yen (VN/HY/2019-ASFV1) and Quynh Phu provinces (VN/QP/2019-ASFV1) in Vietnam in 2019, and the subsequent outbreak in Hung Yen (VN/HY/2022-ASFV2) in 2022, to those of other ASFV strains. VN/HY/2019-ASFV1, VN/QP/2019-ASFV1, and VN/HY/2022-ASFV2 genomes were 189,113, 189,081, and 189,607 bp in length, encoding 196, 196, and 203 open reading frames (ORFs), respectively. VN/HY/2019-ASFV1 and VN/QP/2019-ASFV1 shared a 99.91–99.99% average nucleotide identity with genotype II strains. Variations were identified in 28 ORFs in VN/HY/2019-ASFV1 and VN/QP/2019-ASFV1 compared to 20 ASFV strains, and 16 ORFs in VN/HY/2022-ASFV2 compared to VN/HY/2019-ASFV1 and VN/QP/2019-ASFV1. Vietnamese ASFV genomes were classified as IGR II variants between the I73R and I329L genes, with two copy tandem repeats between the A179L and A137R genes. A phylogenetic analysis based on the whole genomes of 27 ASFV strains indicated that the Vietnamese ASFV strains are genetically related to Estonia 2014, ASFV-SY18, and Russia/Odintsovo_02/14. These results reveal the complete genome sequences of ASFV circulating during the first outbreak in 2019, providing important insights into understanding the evolution, transmission, and genetic variation of ASFV in Vietnam.
Cemiplimab in Ultra-Octogenarian Patients with Cutaneous Squamous Cell Carcinoma: The Real-Life Experience of a Tertiary Referral Center
Authors: Denaro Nerina , Passoni Emanuela , Indini Alice , Nazzaro Gianluca , Beltramini Giada Anna , Benzecry Valentina , Colombo Giuseppe , Cauchi Carolina , Solinas Cinzia , Scartozzi Mario , Marzano Angelo Valerio , Garrone Ornella
Published: September 2023

Background: The incidence of cutaneous squamous cell carcinoma (cSCC) is rapidly increasing, paralleling the aging of the population. cSCC predominantly affects chronically sun-exposed areas, such as the head and neck region. At our tertiary center, a multidisciplinary approach to non-melanoma skin cancer is provided for locally advanced cSCC. Methods: We retrospectively revised all patients with locally advanced/metastatic cSCC treated with anti-PD1 antibody (Cemiplimab) at our Institution from January 2020 to March 2023 (minimum follow-up of 4 months on treatment). Results: Overall, we consecutively treated 20 ultra-octogenarian patients, of whom 15 were males and 5 were females (median age: 86.9 years). Despite age, a median number of concomitant drugs, and comorbidities, efficacy, and safety were superimposable with the available literature. No patients reported treatment-related adverse events of grade 3 or higher. Grade 2 adverse events were reported in 25% of patients. Overall, the response rate was 65%, with 50% partial responses and 20% long-lasting stable disease. The median duration of response was 14 months. The G8 elderly score was assessed in all patients, and the median score was 12 (range 9–14). Conclusions: Among ultra-octogenarian patients, a clinical benefit from Cemiplimab was obtained in most, including tumor shrinkage and pain relief. Cemiplimab confirmed its effectiveness in elderly patients in a real-life setting, with no new safety concerns.
The Administration of Inactivated and Stabilized Whole-Cells of Saccharomyces cerevisiae to Gestating Sows Improves Lactation Efficiency and Post-Weaning Antimicrobial Use
Authors: Scollo Annalisa , Borello Irene , Ghilardi Marco , Cavagnini Alberto
Published: September 2023

Increasingly hyperprolific sows and the need to reduce antibiotics represent challenges in pig farming. The aim of this work was to determine the effects of a postbiotic obtained from inactivated and stabilized whole-cells of Saccharomyces cerevisiae, administered during the sow’s gestation, on the performance of the mother and litter. Maternal feed intake, productive parameters, colostrum quality and post-weaning piglets’ health were assessed, including antibiotic consumption. The trial involved 183 sows, divided into two groups: (1) sows fed with a daily supplementation of postbiotic during gestation (n = 90); (2) sows without any supplement (n = 93). Piglets were followed up at two different post-weaning sites. The lactation efficiency of the treated sows improved by +5.9% (41.3 ± 11.4 vs. 35.4 ± 11.6%; p = 0.011). Lactating piglets’ mortality was lower in the treated group (25.1 ± 16.7 vs. 28.8 ± 14.4%; p = 0.048). The same tendency was shown in both the weaning sites, together with a reduced antibiotic consumption in weaning site 1 (0.72 ± 0.25 vs. 1.22 ± 0.30 DDDvet/PCU; p = 0.047). The results suggest the role of this postbiotic administered to the mother in improving the health status of the piglets. Furthermore, lactation efficiency is suggested as an interesting parameter for assessing the efficiency of farming.
Vibrational Effects on the Acoustic Performance of Multi-Layered Micro-Perforated Metamaterials
Authors: Maury Cédric , Bravo Teresa
Published: September 2023

Broadband noise reduction over the low–mid frequency range in the building and transportation sectors requires compact lightweight sound absorbers of a typical subwavelength size. The use of multi-layered, closely spaced (micro-)perforated membranes or panels, if suitably optimized, contributes to these objectives. However, their elasticity or modal behaviors often impede the final acoustical performance of the partition. The objective of this study is to obtain insights into the vibrational effects induced by elastic limp membranes or panel volumetric modes on the optimized sound absorption properties of acoustic fishnets and functionally graded partitions (FGP). The cost-efficient global optimization of the partitions’ frequency-averaged dissipation is achieved using the simulated annealing optimization method, while vibrational effects are included through an impedance translation method. A critical coupling analysis reveals how the membranes or panel vibrations redistribute the locations of the Hole-Cavity resonances, as well as their cross-coupling with the panels’ first volumetric mode. It is found that elastic limp micro-perforated membranes broaden the pass-band of acoustic fishnets, while smoothing out the dissipation ripples over the FGP optimization bandwidth. Moreover, the resonance frequency of the first panels mode sets an upper limit to the broadband optimization of FGPs, up to which a high dissipation, high absorption, and low transmission can be achieved.