bluuebunny/crossref_metadata_2025 huggingface dataset:
Pharmacovigilance of Risankizumab in the Treatment of Psoriasis and Arthritic Psoriasis: Real-World Data from EudraVigilance Database
Authors: Calapai Fabrizio , Ammendolia Ilaria , Cardia Luigi , Currò Mariaconcetta , Calapai Gioacchino , Esposito Emanuela , Mannucci Carmen
Published: July 2023

Risankizumab is a selective, humanized immunoglobulin G1 (IgG1) monoclonal anti-body directed against interleukin (IL)-23 protein. The therapeutic indication of risankizumab is moderate-to-severe plaque psoriasis and psoriatic arthritis. The safety profile of risankizumab is currently defined by data obtained with clinical trials used for the authorization of entry into the market. The aim of this study was to expand information on the safety of risankizumab through a descriptive post-marketing analysis of real-world data regarding serious adverse reactions (SARs) to risankizumab found in the EudraVigilance database. The EudraVigilance database system, containing SARs linked to drugs not yet licensed for the market in the European Union (EU), was used. In EudraVigilance, SARs are described in single individual cases safety reports (ICSRs). More frequently reported serious SARs to risankizumab are associated with, in descending order, infections, cancer, nervous system disorders, cardiac disorders, abnormal laboratory results, pulmonary disorders, conditions aggravated, and skin disorders. Despite the classical limitations of this post-marketing study (lack of denominator, no certainty of causal relationship between the drug and the adverse reaction), analysis of real-world data related to SARs to risankizumab confirms the known safety profile of the drug but, at the same time, stimulates to further go into detail about the occurrence as adverse reactions of malignancies and their sex distribution.
The Design of a Multilayer and Planar Metamaterial with the Multi-Functions of a High-Absorptivity and Ultra-Broadband Absorber and a Narrowband Sensor
Authors: Peng Guoxiang , Ke Pei-Xiu , Tseng Ling-Chieh , Yang Cheng-Fu , Chen Hung-Cheng
Published: July 2023

The aim of this study is to enhance the design of a multilayer and planar metamaterial that serves multiple functions, including high efficiency and ultra-broadband absorption, as well as acting as a narrowband sensor. The primary feature of this absorber is its fully planar structure, which enables the flexible utilization of two distinct absorption functionalities: ultra-broadband absorption, achieved through the application of the MgF2 layer, and narrowband absorption, achieved through the implementation of the Cu layer. To conduct the simulation analyses, COMSOL Multiphysics® simulation software (version 6.0) was employed. The initial innovation lies in the fact that upon irradiation of normal incident light on MgF2 side, the material exhibited an exceptional average absorptivity of 97.0% across an ultra-broadband range spanning from 410 to approximately 2300 nm. Moreover, when the same normal incident light was radiated on the Cu side, the material demonstrated a distinct peak at a precise wavelength of 480 nm, accompanied by an absorptivity of 95.66%. Notably, these results were obtained with the added benefit of angle insensitivity. Such characteristics arise due to the multiple excitation of diverse resonant modes facilitated by the localized surface plasmon resonance and metal–insulator–metal Fabry–Perot cavity. The second innovation focuses on demonstrating that MgF2 can serve as an effective anti-reflection layer, enhancing the absorptivity of the ultra-broadband absorber. The third innovation aims to establish that Cu is the optimal metal choice. Even substituting Cu with other metals did not diminish the absorptivity of the ultra-broadband absorber; it should be noted that alternative metals might negatively impact the absorptivity of the narrowband absorber.
Effect of Plant Defenses and Plant Nutrients on the Performance of Specialist and Generalist Herbivores of Datura: A Macroevolutionary Study
Authors: Kariñho-Betancourt Eunice , Vázquez-Lobo Alejandra , Núñez-Farfán Juan
Published: July 2023

Macroevolutionary patterns in the association between plant species and their herbivores result from ecological divergence promoted by, among other factors, plants’ defenses and nutritional quality, and herbivore adaptations. Here, we assessed the performance of the herbivores Lema trilineata daturaphila, a trophic specialist on Datura, and Spodoptera frugiperda, a polyphagous pest herbivore, when fed with species of Datura. We used comparative phylogenetics and multivariate methods to examine the effects of Datura species’ tropane alkaloids, leaf trichomes, and plant macronutrients on the two herbivores´ performances (amount of food consumed, number of damaged leaves, larval biomass increment, and larval growth efficiency). The results indicate that species of Datura do vary in their general suitability as food host for the two herbivores. Overall, the specialist performs better than the generalist herbivore across Datura species, and performance of both herbivores is associated with suites of plant defenses and nutrient characteristics. Leaf trichomes and major alkaloids of the Datura species are strongly related to herbivores’ food consumption and biomass increase. Although hyoscyamine better predicts the key components of the performance of the specialist herbivore, scopolamine better predicts the performance of the generalist; however, only leaf trichomes are implicated in most performance components of the two herbivores. Nutrient quality more widely predicts the performance of the generalist herbivore. The contrasting effects of plant traits and the performances of herbivores could be related to adaptive differences to cope with plant toxins and achieve nutrient balance and evolutionary trade-offs and synergisms between plant traits to deal with a diverse community of herbivores.
Efficient Genomic Prediction of Yield and Dry Matter in Hybrid Potato
Authors: Adams James , de Vries Michiel , van Eeuwijk Fred
Published: July 2023

There is an ongoing endeavor within the potato breeding sector to rapidly adapt potato from a clonal polyploid crop to a diploid hybrid potato crop. While hybrid breeding allows for the efficient generation and selection of parental lines, it also increases breeding program complexity and results in longer breeding cycles. Over the past two decades, genomic prediction has revolutionized hybrid crop breeding through shorter breeding cycles, lower phenotyping costs, and better population improvement, resulting in increased genetic gains for genetically complex traits. In order to accelerate the genetic gains in hybrid potato, the proper implementation of genomic prediction is a crucial milestone in the rapid improvement of this crop. The authors of this paper set out to test genomic prediction in hybrid potato using current genotyped material with two alternative models: one model that predicts the general combining ability effects (GCA) and another which predicts both the general and specific combining ability effects (GCA+SCA). Using a training set comprising 769 hybrids and 456 genotyped parental lines, we found that reasonable a prediction accuracy could be achieved for most phenotypes with both zero common parents (ρ=0.36−0.61) and one (ρ=0.50−0.68) common parent between the training and test sets. There was no benefit with the inclusion of non-additive genetic effects in the GCA+SCA model despite SCA variance contributing between 9% and 19% of the total genetic variance. Genotype-by-environment interactions, while present, did not appear to affect the prediction accuracy, though prediction errors did vary across the trial’s targets. These results suggest that genomically estimated breeding values on parental lines are sufficient for hybrid yield prediction.
Optimization of the Green Chemistry-like Extraction of Phenolic Compounds from Grape (Vitis labrusca L.) and Blackberry (Rubus fruticosus L.) Seeds with Concomitant Biological and Antioxidant Activity Assessments
Authors: Junior Tufy Kabbas , de Moura Cristiane , Cruz Thiago Mendanha , Marques Mariza Boscacci , Carmo Mariana Araújo Vieira do , Deolindo Carolina Turnes Pasini , Daguer Heitor , Azevedo Luciana , Granato Daniel
Published: July 2023

The objective of this work was to determine the phenolic composition, chemical and cellular antioxidant activity, cytotoxicity in human cells, and peroxidative inhibition of the defatted fraction of grape (Vitis labrusca) and blackberry (Rubus fruticosus) seeds. Soxhlet extraction (Sox) was used to extract the fat and obtain the degreased material. A statistical optimization study was developed to maximize the extraction of bioactive compounds and antioxidant activity from defatted grape and blackberry seeds. Simultaneous optimization was applied with a combination of 35.9 min of extraction and a solid-to-solvent ratio of 1 g of defatted grape seed to 61.28 mL of an extracting solvent (60% ethanol) and 62.1 min of extraction and a solid-to-solvent ratio of 1 g of defatted blackberry seed to 64.1 mL of an extracting solvent (60% ethanol). In the cell viability assay, HepG2 cancer cells seemed more sensitive to grape and blackberry extracts, while Ea.hy926 hybrid cells showed more resistance to their effects. In general, the extracts presented low/no cytotoxicity, exhibited a protective effect against H2O2-induced ROS production, and demonstrated antioxidant activity and a protective effect on the erythrocytes when subjected to hypotonic and isotonic conditions not presenting hemolytic behavior (5.0 to 10.0 μg GAE/mL). Thus, the results provided a broad assessment of the bioactivity of the extracts obtained using a simple and low-cost process developed by employing non-toxic solvents and with the potential to be used in technological applications.
Phylogeny and Flow Cytometry of the Genus Kalidium Moq. (Amaranthaceae s.l.) in Kazakhstan
Authors: Osmonali B. B. , Vesselova P. V. , Kudabayeva G. M. , Skaptsov M. V. , Shmakov A. I. , Friesen N.
Published: July 2023

The article presents data on phylogeny, genome size, and ploidy of species of the genus Kalidium Moq. in the flora of Kazakhstan. Genus Kalidium belongs to the tribe Salicornieae of the subfamily Salicornioideae of the family Chenopodiaceae and unites eight species, the main range of which covers the Iranian-Turanian and Central Asian deserts. There are four species in the flora of Kazakhstan: K. foliatum, K. caspicum, K. schrenkianum, and the recently described K. juniperinum. Populations of species of the genus Kalidium in the saline deserts of Kazakhstan occupy large areas, often forming monodominant communities. Sometimes there is a joint growth of two and very rarely three species of the genus. During the period of fieldwork (2021–2022), populations were identified in which these species grew together with a predominance, in most cases, of K. caspicum. Samples of representatives from 15 populations were collected for research. Selected plant samples were studied by flow cytometry to determine plant ploidy. Sequencing of nrITS and two chloroplast fragments were used to build a phylogenetic tree, including sequences from the NCBI database., A phylogenetic tree of species of the genus Kalidium was compiled, which takes previously published data into consideration. In the valley of the middle reaches of the Syrdarya River, tetraploid populations of K. caspicum were found. A hybrid between K. foliatum and K. caspicum was found in the Ili River valley (Almaty region, Uigur district). To identify phylogenetic processes at the intraspecific level, the SCoT (Start codon targeted) fingerprinting method was used.
Optimization Control Strategy for a Central Air Conditioning System Based on AFUCB-DQN
Authors: Tian He , Feng Mingwen , Fan Huaicong , Cao Ranran , Gao Qiang
Published: July 2023

The central air conditioning system accounts for 50% of the building energy consumption, and the cold source system accounts for more than 60% of the total energy consumption of the central air conditioning system. Therefore, it is crucial to solve the optimal control strategy of the cold source system according to the cooling load demand, and adjust the operating parameters in time to achieve low energy consumption and high efficiency. Due to the complex and changeable characteristics of the central air conditioning system, it is often difficult to achieve ideal results using traditional control methods. In order to solve this problem, this study first coupled the building cooling load simulation environment and the cold source system simulation environment to build a central air conditioning system simulation environment. Secondly, noise interference was introduced to reduce the gap between the simulated environment and the actual environment, and improve the robustness of the environment. Finally, combined with deep reinforcement learning, an optimal control strategy for the central air conditioning system is proposed. Aiming at the simulation environment of the central air conditioning system, a new model-free algorithm is proposed, called the dominant function upper confidence bound deep Q-network (AFUCB-DQN). The algorithm combines the advantages of an advantage function and an upper confidence bound algorithm to balance the relationship between exploration and exploitation, so as to achieve a better control strategy search. Compared with the traditional deep Q-network (DQN) algorithm, double deep Q-network (DDQN) algorithm, and the distributed double deep Q-network (D3QN) algorithm, the AFUCB-DQN algorithm has more stable convergence, faster convergence speed, and higher reward. In this study, significant energy savings of 21.5%, 21.4%, and 22.3% were obtained by conducting experiments at indoor thermal comfort levels of 24 °C, 25 °C, and 26 °C in the summer.
Progress of Research on Antioxidants and Carriers for Skin Wound Repair
Authors: Zhu Xiaoxuan , Yuan Wenlu , Li Zhuozheng , Lin Yin , Li Wenfang , Ji Lei , Wang Dan , Zhang Huafang , Wang Yingshuai
Published: July 2023

The skin is the first protective barrier of the human body, and oxidative damage is one of the main mechanisms of skin injury. Effective antioxidant therapy plays an important role in skin healing. Therefore, exploring antioxidants and suitable drug delivery methods that can be used for skin injury repair is of great value in regulating skin repair and regeneration and promoting wound healing. Based on this, this paper presents a review of the progress of research on (1) antioxidants and (2) antioxidant carriers for skin repair in order to summarize the research results and provide reference for the subsequent development of new drug-carrier structures and new skin repair strategies.
Evolution Game Analysis of Chemical Risk Supervision Based on Special Rectification and Normal Regulation Modes
Authors: Wang Xunqing , Huang Xinpeng , Zhou Hang , Zhang Nan , Sun Xiaoyu
Published: July 2023

Chemical safety is closely related to public health, safety, and environmental concerns. Strengthening chemical safety supervision is not only vital for ensuring safe production but also plays a crucial role in maintaining overall social safety. This paper aims to analyze the evolutionary game strategies between chemical enterprises and government regulators under different regulatory modes, namely, ‘special rectification’ and ‘normal regulation’. The results indicate that under the ‘special rectification’ pattern, the strategic choices of chemical enterprises regarding safety investment rely on the cost–benefit analysis of safety non-investment. Conversely, in the ‘normal regulation’ mode, the decision to invest in safety is based on the comparison between the cost of safety investment and the cost of not investing in safety. Increasing government sanctions encourages chemical enterprises to prioritize safety investment under both supervision modes. Notably, while punishment significantly impacts safety investment behavior under the ‘normal regulation’ mode, it exhibits negligible influence under the ‘special rectification’ pattern. These research findings provide valuable decision-making support for government agencies tasked with effectively supervising the safety production of chemical enterprises.
Gender-Related Differences in the Citation Impact of Scientific Publications and Improving the Authors’ Productivity
Authors: Kuchanskyi Oleksandr , Andrashko Yurii , Biloshchytskyi Andrii , Omirbayev Serik , Mukhatayev Aidos , Biloshchytska Svitlana , Faizullin Adil
Published: July 2023

The article’s purpose is an analysis of the citation impact of scientific publications by authors of different gender compositions. The page method was chosen to calculate the citation impact of scientific publications, and the obtained results allowed to estimate the impact of the scientific publications based on the number of citations. The normalized citation impact is calculated according to nine subsets of scientific publications that correspond to patterns of different gender compositions of authors. Also, these estimates were calculated for each country with which the authors of the publications are affiliated. The Citation database, Network Dataset (Ver. 13), was chosen for the scientometric analysis. The dataset includes more than 5 million scientific publications and 48 million citations. Most of the publications in the dataset are from the STEM field. The results indicate that articles with a predominantly male composition are cited more than articles with a mixed or female composition of authors in this direction. Analysis of advantages in dynamics indicates that in the last decade, in developed countries, there has been a decrease in the connection between the citation impact of scientific publications and the gender composition of their authors. However, the obtained results still confirm the presence of gender inequality in science, which may be related to socioeconomic and cultural characteristics, natural homophily, and other factors that contribute to the appearance of gender gaps. An essential consequence of overcoming these gaps, including in science, is ensuring the rights of people in all their diversity.
Agha Musa Naghiev Karbala’i—The Man and His Multiple Identities: Between National-Azerbaijani, Shi‘i Muslim, and Bahá’í Religious Identities
Authors: Shahvar Soli
Published: July 2023

Aqa Musa Naghiev Karbala’i (1849–1919)—an oil magnate from Baku and a significant figure in the modern history of Baku and Azerbaijan—and his multiple identities are the focus of this article. Alongside his National-Azerbaijani identity, Naghiev’s religious identity was divided between the Shi‘i Muslim and Bahá’í identities. The circumstances in which Naghiev was born and raised, and lived, especially during his adulthood—as a Shi‘i who had converted to the Baha’i faith, living among the majority Shi‘i Muslim population who were generally alien to Baha’is—dictated, at times, the need to carefully maneuver between these two religious identities. The Baha’i principle of hikmat provided Naghiev with the appropriate means for this during his lifetime. The problem, however, is in the way his identity has been engineered and presented in Azerbaijan’s national historical consciousness as an Azerbaijani-Muslim, concealing his Baha’i religious identity, in spite of his public activities for Baha’is and the Baha’i community, which included financing the building of the Baha’i Spiritual Assembly in Baku (Ruhanie) and being the chairman of its management committee. In this article, I explain this complex picture of Naghiev’s multiple identities through the use of primary (documents from national and private archives, and interviews) and secondary sources.
Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing
Authors: Ding Zifeng , Sun Yuxuan , Xu Sijin , Pan Yan , Peng Yanhong , Mao Zebing
Published: July 2023

In recent years, deep learning techniques for processing 3D point cloud data have seen significant advancements, given their unique ability to extract relevant features and handle unstructured data. These techniques find wide-ranging applications in fields like robotics, autonomous vehicles, and various other computer-vision applications. This paper reviews the recent literature on key tasks, including 3D object classification, tracking, pose estimation, segmentation, and point cloud completion. The review discusses the historical development of these methods, explores different model architectures, learning algorithms, and training datasets, and provides a comprehensive summary of the state-of-the-art in this domain. The paper presents a critical evaluation of the current limitations and challenges in the field, and identifies potential areas for future research. Furthermore, the emergence of transformative methodologies like PoinTr and SnowflakeNet is examined, highlighting their contributions and potential impact on the field. The potential cross-disciplinary applications of these techniques are also discussed, underscoring the broad scope and impact of these developments. This review fills a knowledge gap by offering a focused and comprehensive synthesis of recent research on deep learning techniques for 3D point cloud data processing, thereby serving as a useful resource for both novice and experienced researchers in the field.
A Prediction Model of Maize Field Yield Based on the Fusion of Multitemporal and Multimodal UAV Data: A Case Study in Northeast China
Authors: Zhou Wenqi , Song Chao , Liu Cunliang , Fu Qiang , An Tianhao , Wang Yijia , Sun Xiaobo , Wen Nuan , Tang Han , Wang Qi
Published: July 2023

The prediction of crop yield plays a crucial role in national economic development, encompassing grain storage, processing, and grain price trends. Employing multiple sensors to acquire remote sensing data and utilizing machine learning algorithms can enable accurate, fast, and nondestructive yield prediction for maize crops. However, current research heavily relies on single-type remote sensing data and traditional machine learning methods, resulting in the limited robustness of yield prediction models. To address these limitations, this study introduces a field-scale maize yield prediction model named the convolutional neural network–attention–long short-term memory network (CNN-attention-LSTM) model, which utilizes multimodal remote sensing data collected by multispectral and light detection and ranging (LIDAR) sensors mounted on unmanned aerial vehicles (UAVs). The model incorporates meteorological data throughout the crop reproductive stages and employs the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) for the initial part of the vegetative stage (initial part of the V period), the later part of the vegetative stage (later part of the V period), the reproductive stage (R period), and the maturity stage (M period), along with LIDAR data for Point75–100 in the later part of the V period, Point80–100 in the R period, and Point50–100 in the M period, complemented by corresponding meteorological data as inputs. The resulting yield estimation demonstrates exceptional performance, with an R2 value of 0.78 and an rRMSE of 8.27%. These results surpass previous research and validate the effectiveness of multimodal data in enhancing yield prediction models. Furthermore, to assess the superiority of the proposed model, four machine learning algorithms—multiple linear regression (MLR), random forest regression (RF), support vector machine (SVM), and backpropagation (BP)—are compared to the CNN-attention-LSTM model through experimental analysis. The outcomes indicate that all alternative models exhibit inferior prediction accuracy compared to the CNN-attention-LSTM model. Across the test dataset within the study area, the R2 values for various nitrogen fertilizer levels consistently exceed 0.75, illustrating the robustness of the proposed model. This study introduces a novel approach for assessing maize crop yield and provides valuable insights for estimating the yield of other crops.
Improved Method for Estimating Construction Year of Road Bridges by Analyzing Landsat Normalized Difference Water Index 2
Authors: Hamunzala Bennie , Matsumoto Koji , Nagai Kohei
Published: July 2023

The construction year of road bridges plays an important role in bridge management systems. Based on the age of road bridges and other factors, deterministic and probabilistic deterioration models can be used to calculate deterioration rates and predict the future physical condition of road bridges. Two new techniques are proposed in this manuscript for estimating the construction year of road bridges by analyzing the normalized difference water index 2 (NDWI_2). Technique 1 uses both the target bridge point (TBP) and a selected optimal reference control point, while Technique 2 uses only the TBP. Landsat 5 Thematic Mapper NDWI_2 data were analyzed at all 44 road bridges in Nago City, Japan of the bridges’ overall length ≤ 100 m and construction year between 1990 and 2006. The sequential t-test analysis of the regime shift method, at a significance level α = 0.05 and cutoff length l = 2 to l = 27, was used to interpret the estimated construction year from the NDWI_2 for both techniques. Both techniques successfully determined the estimated construction year, which was statistically significant with p-values < 0.05, except for seven road bridges in Technique 1 and one road bridge in Technique 2. The correlation and comparative analysis of the actual and estimated construction years yielded R2 = 0.24 and R2 = 0.33, as well as an average deviation of S = 5.81 years and S = 4.08 years for Technique 1 and Technique 2, respectively. The findings suggest that Technique 2 is more accurate and provides a better estimate than Technique 1. It was observed that, as the cutoff length l increased, the absolute error between the actual and estimated construction year increased. Therefore, as a measure of accuracy, the upper limit of cutoff length l was set to l≤ 12. It was also observed that the increase in the bridge’s overall length and forested area contributed to the accuracy of the results. By using the construction year as one of the inputs into bridge management systems, bridge managers can make more informed decisions about how best to maintain and improve road bridges to ensure user safety and road bridge preservation for the future.
Sentinel-1 Time Series for Predicting Growing Stock Volume of Boreal Forest: Multitemporal Analysis and Feature Selection
Authors: Ge Shaojia , Tomppo Erkki , Rauste Yrjö , McRoberts Ronald E. , Praks Jaan , Gu Hong , Su Weimin , Antropov Oleg
Published: July 2023

Copernicus Sentinel-1 images are widely used for forest mapping and predicting forest growing stock volume (GSV) due to their accessibility. However, certain important aspects related to the use of Sentinel-1 time series have not been thoroughly explored in the literature. These include the impact of image time series length on prediction accuracy, the optimal feature selection approaches, and the best prediction methods. In this study, we conduct an in-depth exploration of the potential of long time series of Sentinel-1 SAR data to predict forest GSV and evaluate the temporal dynamics of the predictions using extensive reference data. Our boreal coniferous forests study site is located near the Hyytiälä forest station in central Finland and covers an area of 2500 km2 with nearly 17,000 stands. We considered several prediction approaches and fine-tuned them to predict GSV in various evaluation scenarios. Our analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate a considerable decrease in the root mean squared errors (RMSEs) of GSV predictions as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE, prediction accuracy with combined images decreased to 75.6 m3/ha. Feature extraction and dimension reduction techniques facilitated the achievement of near-optimal prediction accuracy using only 8–10 images. Examined methods included radiometric contrast, mutual information, improved k-Nearest Neighbors, random forests selection, Lasso, and Wrapper approaches. Lasso was the most optimal, with RMSE reaching 77.1 m3/ha. Finally, we found that using assemblages of eight consecutive images resulted in the greatest accuracy in predicting GSV when initial acquisitions started between September and January.
A Space-Time Partial Differential Equation Based Physics-Guided Neural Network for Sea Surface Temperature Prediction
Authors: Yuan Taikang , Zhu Junxing , Wang Wuxin , Lu Jingze , Wang Xiang , Li Xiaoyong , Ren Kaijun
Published: July 2023

Sea surface temperature (SST) prediction has attracted increasing attention, due to its crucial role in understanding the Earth’s climate and ocean system. Existing SST prediction methods are typically based on either physics-based numerical methods or data-driven methods. Physics-based numerical methods rely on marine physics equations and have stable and explicable outputs, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns. We believe that these two types of method are complementary to each other, and their combination can potentially achieve better performances. In this paper, a space-time partial differential equation (PDE) is employed to form a novel physics-based deep learning framework, named the space-time PDE-guided neural network (STPDE-Net), to predict daily SST. Comprehensive experiments for SST prediction were conducted, and the results proved that our method could outperform the traditional finite-difference forecast method and several state-of-the-art deep learning and physics-guided deep learning methods.
Anti-Leptospira Antibodies in Buffaloes on Marajó Island
Authors: Barbosa José Diomedes , Martins Fernanda Monik Silva , Vieira Eliel Valentim , Silva Ruama Paixão de Lima , Bomjardim Henrique dos Anjos , Silva Marcos Xavier , Salvarani Felipe Masiero
Published: July 2023

Leptospirosis is a zoonotic disease that has a cosmopolitan geographical distribution, reported in domestic and wild animals, which act as reservoirs and contribute to the spread of microorganisms in the environment. In Brazil, studies on the occurrence of leptospirosis in buffaloes in the Amazon Biome are scarce. The objective of this study was to determine the occurrences of antibodies against Leptospira spp., including serovar Hardjo (Bolivia), isolated from cattle in Brazil and not yet tested in buffaloes. A total of 387 blood serum samples of animals from nine municipalities on Marajó Island, State of Pará, northern Brazil, were obtained from a biological sample bank and analyzed using the microscopic agglutination test (MAT). Serology revealed 91.5% (387/354) of the animals tested positive for anti-Leptospira antibodies. The presence of various detected serovars may have been related to the local practice of combined rearing of different livestock species, as well as to the contact with wild animals and rodents from adjacent forest areas, all factors that likely facilitated the epidemiological chain of the disease in buffaloes. Among the serovars tested, the serovar Hardjo (Bolivia) was the most prevalent, which was present in 79.3% of the reactive buffaloes. It was important to carry out serological and bacteriological surveys in order to identify the serovars that occurred in the herds, with the objective of designing efficient strategies to control leptospirosis in the production of buffaloes.
Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics
Authors: Djenna Amir , Barka Ezedin , Benchikh Achouak , Khadir Karima
Published: July 2023

Cybercriminals are becoming increasingly intelligent and aggressive, making them more adept at covering their tracks, and the global epidemic of cybercrime necessitates significant efforts to enhance cybersecurity in a realistic way. The COVID-19 pandemic has accelerated the cybercrime threat landscape. Cybercrime has a significant impact on the gross domestic product (GDP) of every targeted country. It encompasses a broad spectrum of offenses committed online, including hacking; sensitive information theft; phishing; online fraud; modern malware distribution; cyberbullying; cyber espionage; and notably, cyberattacks orchestrated by botnets. This study provides a new collaborative deep learning approach based on unsupervised long short-term memory (LSTM) and supervised convolutional neural network (CNN) models for the early identification and detection of botnet attacks. The proposed work is evaluated using the CTU-13 and IoT-23 datasets. The experimental results demonstrate that the proposed method achieves superior performance, obtaining a very satisfactory success rate (over 98.7%) and a false positive rate of 0.04%. The study facilitates and improves the understanding of cyber threat intelligence, identifies emerging forms of botnet attacks, and enhances forensic investigation procedures.
Weak Spatial Target Extraction Based on Small-Field Optical System
Authors: Zhang Xuguang , Liu Yunmeng , Duan Huixian , Zhang E
Published: July 2023

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small fields in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement in detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi-frame projection. We use bad point repair and background suppression algorithms to preprocess star images. Afterwards, we analyze and enhance the target signal-to-noise ratio (SNR). Then, we use multi-frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescopes. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has a magnitude of about 12 and an SNR of about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as a real-time target recognition algorithm for space-based synchronous orbit detection payloads.
Nonlinear Ultrasonic Imaging for Porosity Evaluation
Authors: De Simone Mario Emanuele , Boccardi Salvatore , Fierro Gian Piero Malfense , Meo Michele
Published: July 2023

The influence of porosity on the mechanical behaviour of composite laminates represents a complex problem that involves many variables. Therefore, the evaluation of the type and volume content of porosity in a composite specimen is important for quality control and for predicting material behaviour during service. A suitable way to evaluate the porosity content in composites is by using nonlinear ultrasonics because of their sensitivity to small cracks. The main objective of this research work is to present an imaging method for the porosity field in composites. Two nonlinear ultrasound techniques are proposed using backscattered signals acquired by a phased array system. The first method was based on the amplitude of the half-harmonic frequency components generated by microbubble reflections, while the second one involved the frequency derivative of the attenuation coefficient, which is proportional to the porosity content in the specimen. Two composite samples with induced porosity were considered in the experimental tests, and the results showed the high accuracy of both methods with respect to a classic C-scan baseline. The attenuation coefficient results showed high accuracy in defining bubble shapes in comparison with the half-harmonic technique when surface effects were neglected.
Vernonia amygdalina Extract Loaded Microspheres for Controlling Phytophthora palmivora
Authors: Chainanta Jiratchaya , Yakajay Kittiporn , Chinda Chanakarn , Intaparn Phikul , To-anun Chaiwat , Tipduangta Pratchaya , Sirithunyalug Busaban , Haituk Sukanya , Nguanhom Jeerapa , Pusadee Tonapha , Karunarathna Anuruddha , Cheewangkoon Ratchadawan
Published: July 2023

The adverse effects of modern fungicide consumption have caused many issues in the agroecosystem. Hence, under sustainable agriculture concepts, it is important to research alternatives to the currently used fungicide. The use of secondary metabolite-containing herbal extracts for treating plant diseases has become the latest trend in sustainable and green agriculture. However, the poor solubility and volatile nature of many compounds cause practical issues when using them in the field. Hence, bioactive compound delivery through nano- or micro-particles has become a successful technique to improve the solubility and delivery of secondary metabolites to targeted sites. In the current study, the ethyl acetate (EtOAc) extract from dried leaves of Vernonia amygdalina was tested against Phytophthora palmivora isolated from Durian (Durio zibethinus) root rot. Further, the potential of enhancing the effect of V. amygdalina EtOAc treatment through microspheres loaded with V. amygdalina EtOAc extract was also investigated. The microspheres encapsulated with Eudragit® E were synthesized under different electrospray conditions to obtain the microspheres with the highest efficacy. The poison media assays evaluated the fungal growth inhibition efficiency of the V. amygdalina EtOAc crude extract and the synthesized microspheres. The study reveals that the V. amygdalina EtOAc extract has the potential to suppress the growth of P. palmivora. Interestingly, the synthesized microspheres showed immense growth inhibition in P. palmivora, with a 61.10 µg/mL decrease in ED50 compared to the direct usage of V. amygdalina EtOAc extract.
Perceived Social Impacts of Protected Areas, Their Influence on Local Public Support and Their Distribution across Social Groups: Evidence from the Eifel National Park, Germany, during the COVID-19 Pandemic
Authors: McGinlay James , Holtvoeth Jens , Begley Alfie , Dörstel Juliana , Kockelmann Anne , Lammertz Michael , Malesios Chrysovalantis , Jones Nikoleta
Published: July 2023

Protected Areas are a cornerstone of biodiversity conservation policies, providing significant benefits both for nature and people. This study investigates the diversity, extent, and social and spatial distribution of the social outcomes of the Eifel National Park in Germany and how these are linked with the level of public support for the park, as well as the effect of COVID-19 on these social impacts. Structured questionnaires were distributed to local residents living inside or near the national park. According to our study the national park is rated positively by the majority of respondents, and provides a range of benefits, but also costs, to local communities. There are also variations in how these impacts are distributed across different sub-communities, between residents and visitors, and across geographical locations in and around the park, and there is a significant relationship between local people’s perceptions of some social impacts and their level of support for the national park’s existence. Evidence from the COVID-19 pandemic suggests that, whilst living in close proximity to the park enhances the quality of life of local residents, any future increases in tourism may need careful planning and management if they are not to erode local people’s quality of life.
Study of Farmers’ Willingness to Participate in Environmental Governance Based on Recycling, Reduction and Resourcing
Authors: Liu Jia , Huang Senwei , Wang Yijia
Published: July 2023

In order to establish a green and low-carbon agricultural economic system based on the principles of recycling, reduction and resource utilization, to promote a comprehensive green transformation of the economic development and to achieve carbon peaking and carbon neutrality, this article examines the farmers’ willingness to participate in rural environmental governance. Through a questionnaire survey in the Fujian, Anhui and Shanxi provinces, this article explores the influence of homogeneous and heterogeneous relationships in social networks on the farmers’ willingness to participate in rural environmental governance using a logit model and tries to reveal the deeper mechanisms. The results show that: (1) heterogeneous relationships have a significant positive effect on farmers’ participation in rural environmental governance, but homogeneous relationships do not have a significant effect. (2) The larger the size of the social network, the weaker the farmers’ willingness to participate in rural environmental governance. (3) Age and education level have significant effects on willingness to participate. The older the age, the weaker the willingness to participate; the higher the education level, the stronger the willingness to participate. (4) The larger the number of family members, the stronger the farmers’ willingness to participate in environmental governance. (5) The subjective cognitive status of farmers also has an important influence on their willingness to participate. The more environmental knowledge is acquired, the stronger the willingness to participate in rural environmental governance. Therefore, to promote rural environmental management, there is an urgent need to keep modern farmers on rural land and to make the countryside a beautiful space for living and working with complete living functions.
Effect of Persistent Organic Pollutants on Human Health in South Korea: A Review of the Reported Diseases
Authors: Rokni Ladan , Rezaei Mehdi , Rafieizonooz Mahdi , Khankhajeh Elnaz , Mohammadi Ali Akbar , Rezania Shahabaldin
Published: July 2023

Persistent organic pollutants (POPs) usually originate from human activities and have been released into the environment for several decades. They are highly resistant to natural decomposition and can accumulate in an organism’s tissues and in all environmental components. Due to their unique characteristics, they have an ability to bio-magnify and bio-accumulate in animals, through the food chain and via inhalation, severely endangering the health of people. As reported, the exposure of humans to POPs causes various health problems such as cancers, diabetes, birth defects, endocrine disruption, cardiovascular diseases and dysfunctional immune and reproductive systems. The residents of South Korea are likely to face a high risk of diseases because of the existence of POPs in the environment. For instance, South Korea’s atmosphere has been reported as a hotspot for POP pollution. Besides, South Koreans’ high amount of seafood consumption is considered another source of POPs. Therefore, this article reviews the status of POP contamination in food and the health impact of POPs in South Korea. Based on the findings, the most-reported diseases were obesity and diabetes, which positively correlated to age, food habits, body index, and level of exposure to POPs. In addition, cancer and metabolic diseases are at an alarming level. Therefore, the public health impacts of POPs need continuous assessment in South Korea over the next decade.
Spatiotemporal Evolution, Coupling Coordination Degree and Obstacle Factors of Urban High-Quality Development: A Case Study of Anhui Province
Authors: Wei Xiaoxue , Zhao Rui , Xu Jie
Published: July 2023

Anhui Province is located in the superposition area of the “Strategy of the Rise of Central China” and the “Strategy of Integrated Development of the Yangtze River Delta”. Analyzing the high-quality development of Anhui Province is crucial for regional development. This paper proposes a comprehensive evaluation system based on the “New development philosophy”. Then, the information entropy method is applied to measure the high-quality development index (HQDI). The Markov transition probability matrix is used to explore the evolutionary trend of the HQDI. This paper also analyzes the coupling coordination relationships between high-quality development subsystems. Finally, the obstacle factor diagnostic model is adopted to find the factors that impede high-quality development. The result shows that: (1) There exists a significant spatial gradient difference in Anhui Province. The overall spatial distribution of the HQDI shows a pattern of “high in the central and east, low in the north and south”. (2) The “club convergence effect” and the “Matthew effect” of urban high-quality development are observed. (3) Subsystem analysis verified the existence of regional differences. Hefei has the leading position in all development subsystems. (4) The coupling coordination degree of subsystems is relatively low, and the problem of disorder development within Anhui Province is significant. (5) The fixed assets’ investment, the total amount of imports and exports, the GDP, the total urban wastewater treatment and the urban road passenger volume are the main obstacle factors.
Rural-Urban Linkages: Regional Financial Business Services’ Integration into Chilean Agri-Food Value Chains
Authors: Sanguinet Eduardo Rodrigues , García-García Francisco de Borja
Published: July 2023

The spatial structure of urban-related industries and agri-food value chains is important for promoting sustainable linkages. Using an interregional input-output framework, this study explores the geography of inter-industry linkages between financial business services and the Chilean agri-food related industries from a subnational perspective. This framework allows adding evidence on rural-related economic activities and financial business services, covering Chilean internal geography, which has a regional concentration of both the business services supply and the agriculturally engaged regions, potentially blocking territorial competitiveness. Our findings indicate that the relationship between value-added and trade is consistent with the vertical fragmentation of domestic production associated with returns to scale. Consequently, while the value-added financial services are more intense in trade for global value chains, this occurs in regions specialized in agri-food industries and services. Our findings also show that Southern Chilean regions engaged in primary agricultural production have lower levels of financial services directly and indirectly embedded in both domestic and global value chains trade, thus highlighting an uneven geography of vertical integration within the country.