bluuebunny/crossref_metadata_2025 huggingface dataset:
Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation
Authors: Zhao Xuepeng , Frech James , Foster Michael , Heidinger Andrew
Published: July 2024

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.
An Adaptive Noisy Label-Correction Method Based on Selective Loss for Hyperspectral Image-Classification Problem
Authors: Li Zina , Yang Xiaorui , Meng Deyu , Cao Xiangyong
Published: July 2024

Due to the intricate terrain and restricted resources, hyperspectral image (HSI) datasets captured in real-world scenarios typically contain noisy labels, which may seriously affect the classification results. To address this issue, we work on a universal method that rectifies the labels first and then trains the classifier with corrected labels. In this study, we relax the common assumption that all training data are potentially corrupted and instead posit the presence of a small set of reliable data points within the training set. Under this framework, we propose a novel label-correction method named adaptive selective loss propagation algorithm (ASLPA). Firstly, the spectral–spatial information is extracted from the hyperspectral image and used to construct the inter-pixel transition probability matrix. Secondly, we construct the trusted set with the known clean data and estimate the proportion of accurate labels within the untrusted set. Then, we enlarge the trusted set according to the estimated proportion and identify an adaptive number of samples with lower loss values from the untrusted set to supplement the trusted set. Finally, we conduct label propagation based on the enlarged trusted set. This approach takes full advantage of label information from the trusted and untrusted sets, and moreover the exploitation on the untrusted set can adjust adaptively according to the estimated noise level. Experimental results on three widely used HSI datasets show that our proposed ASLPA method performs better than the state-of-the-art label-cleaning methods.
Interactions and Conflicts between Urbanization and Greenness: A Case Study from Nanjing, China
Authors: Yang Shengjie , Zhong Liang , Zhou Yunqiao , Sun Bin , Wang Rui , Sun Zhengguo , Li Jianlong
Published: July 2024

Urbanization is rapidly occupying green spaces, making it crucial to understand implicit conflicts between urbanization and greenness. This study proposes an ecological greenness index (EGI) and a comprehensive urbanization index (CUI) and selects Nanjing, a megacity in China, as the study area to research the spatial and temporal evolutionary trends of the EGI and CUI in the context of land use/land cover (LULC) changes from 2000 to 2020. Meanwhile, the conflicts and complex interaction characteristics of the EGI and CUI are discussed from both static and dynamic perspectives, and their driving mechanisms are investigated by combining specific indicators. The results demonstrate that over the past 20 years, LULC in Nanjing was dominated by cultivated land, forest land, and artificial surfaces. The encroachment of artificial surfaces on green space was strengthened, resulting in a decrease in the proportion of cultivated land from 70.09% in 2000 to 58.00% in 2020. The CUI increased at a change rate of 0.6%/year, while the EGI showed significant browning (change rate: −0.23%/year), mainly concentrated within the main urban boundaries. The relationship between the CUI and EGI made the leap from “primary coordination” to “moderate coordination”, but there remains a risk of further deterioration of the decoupling relationship between the CUI and ecological pressures. The multi-year average contribution of the CUI to the EGI was 49.45%. Urbanization activities that dominate changes in greenness have changed over time, reflecting the timing of urban conflict management. The results provide important insights for urban ecological health monitoring and management.
An Adaptive Radar Target Detection Method Based on Alternate Estimation in Power Heterogeneous Clutter
Authors: Xiao Daipeng , Liu Weijian , Chen Hui , Li Hao , Li Binbin
Published: July 2024

Multichannel radars generally need to utilize a certain amount of training samples to estimate the covariance matrix of clutter for target detection. Due to factors such as severe terrain fluctuations and complex electromagnetic environments, the training samples usually have different statistical characteristics from the data to be detected. One of the most common scenarios is that all data have the same clutter covariance matrix structure, while different data have different power mismatches, called power heterogeneous characteristics. For detection problems in the power heterogeneous clutter environments, we propose detectors based on alternate estimation, using the generalized likelihood ratio test (GLRT) criterion, Rao criterion, Wald criterion, Gradient criterion, and Durbin criterion. Monte Carlo simulation experiments and real data indicate that the detector based on the Rao criterion has the highest probability of detection (PD). Furthermore, when signal mismatch occurs, the detector based on the GLRT criterion has the best selectivity, while the detector based on the Durbin criterion has the most robust detection performance.
Flexible Graphene Film-Based Antenna Sensor for Large Strain Monitoring of Steel Structures
Authors: Weng Shun , Zhang Jingqi , Gao Ke , Zhu Hongping , Peng Tingjun
Published: July 2024

In the field of wireless strain monitoring, it is difficult for the traditional metal-made antenna sensor to conform well with steel structures and monitor large strain deformation. To solve this problem, this study proposes a flexible antenna strain sensor based on a ductile graphene film, which features a 6.7% elongation at break and flexibility due to the microscopic wrinkle structure and layered stacking structure of the graphene film. Because of the use of eccentric embedding in the feeding form, the sensor can be miniaturized and can simultaneously monitor strain in two directions. The sensing mechanism of the antenna is analyzed using a void model, and an antenna is designed based on operating frequencies of 3 GHz and 3.5 GHz. The embedding size is optimized using a Smith chart and impedance matching principle. Both the simulation and experimental results verify that the resonant frequency and strain magnitude are linearly inversely proportional. The experimental results show that the strain sensitivity is 1.752 kHz/με along the geometric length and 1.780 kHz/με along the width, with correlation coefficients of 0.99173 and 0.99295, respectively.
Effect of Axial Clearance Variation on Dual-Rotor Flowmeter Performance
Authors: Huang Fuji , Yan Liang , Monsia Chabi Christian , Han Yuxiang , Liu Jiabao , Zan Hao
Published: July 2024

This study examines the impact of axial clearance variations on the performance characteristics of a dual-rotor flowmeter (DRT-FM) through numerical simulations, with the validity of the numerical results verified by calibration experiments. The results indicate that within the range of 200 L/h to 1600 L/h, the K factors of different groups increase as clearance increases. The K factor of the 0.80 mm group is the largest, showing an average increase of around 6% compared to that of the 0.50 mm group. Additionally, Linearity E also decreased, with a minimum of 1.07% in the 0.65 mm group, significantly lower than the 3.33% in the 0.50 mm group. Furthermore, the pressure loss increased slightly, with the 0.65 mm group having the largest pressure loss; however, at a flow rate of 1600 L/h, the pressure loss only increases by 0.186 kPa compared to that of the 0.50 mm group. Flow field analysis reveals that changes in axial clearance predominantly affect pressure distribution. Larger clearances reduce low-pressure regions on upstream and downstream transition surfaces, thereby reducing energy loss due to pressure changes. Entropy analysis further demonstrates that higher axial clearance decreases energy loss in the upstream and downstream stationary domains, optimizing the DRT-FM’s energy characteristics.
Real-Time Detection of Microplastics Using an AI Camera
Authors: Sarker Md Abdul Baset , Imtiaz Masudul H. , Holsen Thomas M. , Baki Abul B. M.
Published: July 2024

Microplastics (MPs, size ≤ 5 mm) have emerged as a significant worldwide concern, threatening marine and freshwater ecosystems, and the lack of MP detection technologies is notable. The main goal of this research is the development of a camera sensor for the detection of MPs and measuring their size and velocity while in motion. This study introduces a novel methodology involving computer vision and artificial intelligence (AI) for the detection of MPs. Three different camera systems, including fixed-focus 2D and autofocus (2D and 3D), were implemented and compared. A YOLOv5-based object detection model was used to detect MPs in the captured image. DeepSORT was then implemented for tracking MPs through consecutive images. In real-time testing in a laboratory flume setting, the precision in MP counting was found to be 97%, and during field testing in a local river, the precision was 96%. This study provides foundational insights into utilizing AI for detecting MPs in different environmental settings, contributing to more effective efforts and strategies for managing and mitigating MP pollution.
Underwater Single-Photon 3D Reconstruction Algorithm Based on K-Nearest Neighbor
Authors: Wang Hui , Qiu Su , Lu Taoran , Kuang Yanjin , Jin Weiqi
Published: July 2024

The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time–photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment. Therefore, new reconstruction methods are necessary for underwater SPAD detection to obtain high-resolution images. In this paper, we propose an algorithm that reconstructs high-resolution depth profiles of underwater targets from a time–photon histogram by employing the K-nearest neighbor (KNN) to classify multiple targets and the background. The results contribute to the performance of pixel accumulation and depth estimation algorithms such as pixel cross-correlation and ManiPoP. We use public experimental data sets and underwater simulation data to verify the effectiveness of the proposed algorithm. The results of our algorithm show that the root mean square errors (RMSEs) of land targets and simulated underwater targets are reduced by 57.12% and 23.45%, respectively, achieving high-resolution single-photon depth profile reconstruction.
A Semi-Supervised Adaptive Matrix Machine Approach for Fault Diagnosis in Railway Switch Machine
Authors: Li Wenqing , Xu Zhongwei , Mei Meng , Lan Meng , Liu Chuanzhen , Gao Xiao
Published: July 2024

The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model’s optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data.
Smart Buildings: A Comprehensive Systematic Literature Review on Data-Driven Building Management Systems
Authors: Taboada-Orozco Adrian , Yetongnon Kokou , Nicolle Christophe
Published: July 2024

Buildings are complex structures composed of heterogeneous elements; these require building management systems (BMSs) to dynamically adapt them to occupants’ needs and leverage building resources. The fast growth of information and communication technologies (ICTs) has transformed the BMS field into a multidisciplinary one. Consequently, this has caused several research papers on data-driven solutions to require examination and classification. This paper provides a broad overview of BMS by conducting a systematic literature review (SLR) summarizing current trends in this field. Unlike similar reviews, this SLR provides a rigorous methodology to review current research from a computer science perspective. Therefore, our goal is four-fold: (i) Identify the main topics in the field of building; (ii) Identify the recent data-driven methods; (iii) Understand the BMS’s underlying computing architecture (iv) Understand the features of BMS that contribute to the smartization of buildings. The result synthesizes our findings and provides research directions for further research.
Optimization of Extraction Process of Total Alkaloids from Thalictrum delavayi Franch. and Their Therapeutic Potential on Pulmonary Infection Caused by Klebsiella pneumoniae and Escherichia coli
Authors: Chen Li , Aku Mochezai , Xia Zhaobin , Yang Shiyu , Yang Danjiao , Chen Chaoxi
Published: July 2024

Bacterial co-infected pneumonia is an acute inflammatory reaction of the lungs mainly caused by Gram-negative bacteria. Antibiotics are urgently important but have the disadvantage of antibacterial resistance, and alternative treatments with medicinal plants are attractive. On the Qinghai–Tibet Plateau, Thalictrum delavayi Franch. (T. delavayi) is an important member of the buttercup family (Ranunculaceae), is rich in alkaloids and has been used in folk medicine for thousands of years. In this study, the extraction process of total alkaloids from the whole T. delavayi plant was optimized and the extract’s therapeutic potential against pulmonary infection caused by Klebsiella pneumoniae and Escherichia coli was investigated. The results showed that the optimum experimental conditions for the total alkaloids (2.46%) from T. delavayi were as follows: hydrochloric acid volume fraction of 0.8%, solid–liquid ratio of 1:12 and sonication time of 54 min. The treatment reduced bacterial counts, white blood cell counts and inflammatory cell classification in bronchoalveolar lavage fluid (BALF) and the levels of inflammatory cytokines interleukin-4 (IL-4), interleukin-6 (IL-6), tumor necrosis factor α (TNF-α), procalcitonin (PCT) and C-reactive protein (CRP), procalcitonin (PCT) and C-reactive protein (CRP) in the serum in experimental groups. The results in our experimental preliminary work suggested that the total alkaloids from T. delavayi had therapeutic effects on mice with Klebsiella pneumoniae and Escherichia coli mixed infectious pneumonia, providing experimental support for the plant’s therapeutic potential in treating pulmonary infections caused by Klebsiella pneumoniae and Escherichia coli.
Enzymatic Diagnostics of Soil Health of the European Part of Russia with Lead Contamination
Authors: Minnikova Tatiana , Kolesnikov Sergey , Kuzina Anna , Trufanov Dmitry , Khrapay Ekaterina , Trushkov Anatoly
Published: July 2024

Lead (Pb) is one of the most common environmental pollutants. Lead has an acute toxic effect on soil biotas and the enzymatic system of soils. The objective of this study is to carry out enzymatic diagnostics of soil health in the European part of Russia after Pb contamination. As a part of the simulation experiment, Pb (at maximum permissible concentrations (MPCs) of 1, 10, and 100) was used to contaminate 12 types of soils in the south and center of the European part of Russia, which differed in their physical and chemical properties. To assess soil health, the activity of oxidoreductases (catalase, dehydrogenases, and cysteine reductase) and hydrolases (invertase, urease, and phosphatase) was studied. Most enzymes were inhibited with increased Pb dosage. The most sensitive soils to Pb contamination, assessed by enzyme activity, are soils of semi-deserts and dry steppes. Cysteine reductase is considered the most sensitive enzyme to Pb contamination. The most informative indicators for Pb contamination were phosphatase, cysteine reductase, and invertase. The P (phosphatase) cycle and the redox enzyme (catalase) also have instability in Pb-contaminated soils. The C (invertase and dehydrogenases) and N (urease) cycles do not change significantly when contaminated with lead. The results of this study can be used for the diagnostics of the condition of soils in different natural areas after Pb contamination.
Tracing Soil Contamination from Pre-Roman Slags at the Monte Romero Archaeological Site, Southwest Spain
Authors: Fernández-Caliani Juan Carlos , Pérez-Macías Juan Aurelio
Published: July 2024

Soil serves as a repository of human history, preserving artifacts within its horizons. However, the presence of chemically reactive remnants, such as ancient slags, can significantly impact the surrounding soil environment. This paper addresses this scarcely explored issue by focusing on soil contamination arising from pre-Roman slag deposits at the Monte Romero archaeological site in southwest Spain, dating back to the Tartessian period (c. 7th century BC). Through the high-resolution microscopy examination of slag wastes and the trace element analysis of soil samples by ICP-OES, this study evaluated current contamination status using a multi-index approach. The results revealed markedly high levels of Pb (>5000 mg kg−1), Cu (up to 2730 mg kg−1), and As (up to 445 mg kg−1) in the soil compared to a control sample. The identification of secondary complex compounds like Cu arsenates and Pb arsenates/antimonates within slag cavities suggests post-depositional weathering processes, leading to the dispersion of potentially toxic elements into the surrounding soil. Assessments through indices of contamination and potential ecological risk highlighted severe contamination, particularly concerning Ag, Pb, Sb, Cu, and As. This study underscores the importance of addressing potential environmental hazards associated with archaeological sites hosting remnants of metal production.
Protein and Leucine Requirements for Maximal Muscular Development and Athletic Performance Are Achieved with Completely Plant-Based Diets Modeled to Meet Energy Needs in Adult Male Rugby Players
Authors: Goldman David , Warbeck Cassandra , Karlsen Micaela
Published: July 2024

Rugby athletes consume large amounts of animal protein in accordance with conventional dietary guidance to increase muscle mass and strength. This misaligns with national dietary guidelines, which suggest limiting meat consumption for chronic disease prevention. The ability of completely plant-based diets to satisfy the nutritional needs of rugby players has not been explored. This study scaled nutrient data from a large population consuming completely plant-based diets with limited supplemental protein to meet the calorie requirements of adult male rugby athletes to assess whether protein and leucine recommendations for muscular development and athletic performance would be achieved. Calorie requirements were estimated from research that employed the doubly labeled water method, and dietary data from the Adventist Health Study-2 were scaled to this level. The modeled protein level was 1.68 g/kg/day, which meets recommendations for maximal gains in muscle mass, strength, and athletic performance. The modeled leucine level was 2.9 g/meal for four daily meals, which exceeds the threshold proposed to maximally stimulate muscle protein synthesis in young men. These results indicate that consuming large portions of completely plant-based meals can satisfy protein and leucine requirements for maximal muscular development and athletic performance in adult male rugby athletes while aligning with public health recommendations.
Evaluation and Prediction of Carbon Storage in the Qinghai-Tibet Plateau by Coupling the GMMOP and PLUS Models
Authors: Yuan Li , Xu Jing , Feng Binrui
Published: July 2024

Land-use alterations exert a profound impact on carbon storage within terrestrial ecosystems. Exploring the spatiotemporal dynamics of regional land use and carbon storage is crucial for optimizing national spatial planning and fostering low-carbon development. For this study, we utilized land-use data spanning from 2000 to 2020 for the Tibetan Plateau and assessed the spatial and temporal variations in carbon storage using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. We adjusted the carbon density within the provinces in the study area as a prerequisite. Moreover, we integrated the Grey Multi-objective Decision-making (GMMOP) model with the Patch-generating Land-use Simulation (PLUS) model to forecast carbon storage alterations in 2030 across various scenarios. The findings indicated that between 2000 and 2020, the overall carbon storage witnessed a decrease of 18.94 × 108 t. Carbon storage in grassland decreased by 22.10 × 108 t, and carbon storage in unused land, forest land, cultivated land, construction land, and water increased by 1.56 × 108 t, 0.92 × 108 t, 0.66 × 108 t, 158.50 × 104 t and 26.74 × 104 t, respectively. The soil organic carbon pool exhibited the highest average carbon storage of 195.63 × 108 t, whereas the litterfall organic carbon pool contained the lowest average carbon stock of 15.07 × 108 t. In comparison with the levels observed in 2020, the total carbon storage experienced a reduction of 8.66 × 108 t and 5.29 × 108 t under the inherent progression and economic growth scenarios, respectively. Conversely, it rose by 11.87 × 108 t and 16.21 × 108 t under the environmental preservation and holistic progression scenarios, respectively. Under the holistic progression scenario, the belowground biomass organic carbon pool exhibited the highest carbon storage increase of 5.59%. These findings offer valuable insights for the management and enhancement of carbon sinks in the Qinghai-Tibet Plateau.
Review and Classification of Objectives in Dynamic Dial-a-Ride Systems: A Triple Bottom Line Approach of Sustainability
Authors: Tiwari Sapan , Nassir Neema , Lavieri Patricia Sauri
Published: July 2024

Dynamic dial-a-ride problems (DDARPs) involve designing routes and schedules for customers with specific origins and destinations. While the optimization of DDARPs has been extensively examined, these analyses often focus solely on economic decisions. The recent literature emphasizes the inclusion of social and environmental factors in addition to economic considerations for a sustainable transportation system. This paper provides a conceptual review that identifies and classifies the most common DDARP objectives in the three dimensions of the Triple-Bottom-Line (3BL) approach of sustainability: environmental, economic, and social. This study analyzes the interconnections among different objectives and provides insights into multi-objective approaches used in transportation problems. The findings demonstrate the interconnectedness of objectives from different dimensions and highlight the involvement of various stakeholders in decision-making. The results show that optimizing one objective may have implications for other objectives, suggesting a trade-off to be considered. The results reveal that social objectives boost the economic dimension by improving service quality; however, environmental objectives negatively impact the economic dimension. Additionally, a geographical analysis was conducted, which revealed continent-wise variations in research focus and contributions. Future studies should focus more on the social and environmental dimensions to promote a sustainable transportation system.
Research on Energy Trading Mechanism Based on Individual Level Carbon Quota
Authors: Wang Di , Zhao Daozhi , Chen Fang , Tang Xin
Published: July 2024

High economic growth is accompanied by substantial consumption of fossil energy and significant negative externalities on the ecological environment. The global warming effect resulting from environmental pollution caused by energy has brought energy carbon emissions into the forefront of social attention. Establishing a carbon trading market is an essential measure to achieve the “double carbon” goal, with individual and household carbon emissions accounting for 70% of China’s total emissions. Constructing an individual-level carbon trading market will facilitate the efficient realization of this goal. However, addressing the challenge of handling vast amounts of data and network congestion in relation to frequent but small-scale individual carbon trading has become an urgent issue that needs to be resolved. In light of this, the present study designs a digital technology-based framework for the carbon market trading system and proposes an individual carbon asset price-based model for carbon market trading, aiming to establish a research framework for the carbon quota market. Furthermore, blockchain technology is employed as the underlying technology in the proposed carbon trading market model to cater to individual-level carbon trading services and achieve optimal matching between carbon quota suppliers, thereby enhancing profitability of the carbon trading platform. The numerical results obtained from the model demonstrate that in absence of government subsidy mechanisms, individual-level carbon trading can effectively reduce total consumer emissions. The present study successfully overcomes the carbon lock-in effect of consumer groups and achieves the generation and trading of individual carbon assets despite capital constraints. This study facilitates accumulation and trade of individual carbon resources, reduces overall consumer emissions, enhances environmental benefits at societal level, and provides a foundation for governmental decision-making.
Study on the Effect of Burial Depth on Selection of Optimal Intensity Measures for Advanced Fragility Analysis of Horseshoe-Shaped Tunnels in Soft Soil
Authors: Du Tao , Zhang Tongwei , Zhou Shudong , Zhang Jinghan , Zhang Yi , Li Weijia
Published: July 2024

Seismic intensity measures (IMs) can directly affect the seismic risk assessment and the response characteristics of underground structures, especially when considering the key variable of burial depth. This means that the optimal seismic IMs must be selected to match the underground structure under different buried depth conditions. In the field of seismic engineering design, peak ground acceleration (PGA) is widely recognized as the optimal IM, especially in the seismic design code for aboveground structures. However, for the seismic evaluation of underground structures, the applicability and effectiveness still face certain doubts and discussions. In addition, the adverse effects of earthquakes on tunnels in soft soil are particularly prominent. This study aims to determine the optimal IMs applicable to different burial depths for horseshoe-shaped tunnels in soft soil using a nonlinear dynamic time history analysis method, and based on this, establish the seismic fragility curves that can accurately predict the probability of tunnel damage. The nonlinear finite element analysis model for the soil–tunnel interaction system was established. The effects of different burial depths on damage to horseshoe-shaped tunnels in soft soil were systematically studied. By adopting the incremental dynamic analysis (IDA) method and assessing the correlation, efficiency, practicality, and proficiency of the potential IMs, the optimal IMs were determined. The analysis indicates that PGA emerges as the optimal IM for shallow tunnels, whereas peak ground velocity (PGV) stands as the optimal IM for medium-depth tunnels. Furthermore, for deep tunnels, velocity spectral intensity (VSI) emerges as the optimal IM. Finally, the seismic fragility curves for horseshoe-shaped tunnels in soft soil were built. The proposed fragility curves can provide a quantitative tool for evaluating seismic disaster risk, and are of great significance for improving the overall seismic resistance and disaster resilience of society.
Performance Study on the Use of Genetic Algorithm for Reducing Feature Dimensionality in an Embedded Intrusion Detection System
Authors: Silva João Lobo , Fernandes Rui , Lopes Nuno
Published: July 2024

Intrusion Detection Systems play a crucial role in a network. They can detect different network attacks and raise warnings on them. Machine Learning-based IDSs are trained on datasets that, due to the context, are inherently large, since they can contain network traffic from different time periods and often include a large number of features. In this paper, we present two contributions: the study of the importance of Feature Selection when using an IDS dataset, while striking a balance between performance and the number of features; and the study of the feasibility of using a low-capacity device, the Nvidia Jetson Nano, to implement an IDS. The results, comparing the GA with other well-known techniques in Feature Selection and Dimensionality Reduction, show that the GA has the best F1-score of 76%, among all feature/dimension sizes. Although the processing time to find the optimal set of features surpasses other methods, we observed that the reduction in the number of features decreases the GA processing time without a significant impact on the F1-score. The Jetson Nano allows the classification of network traffic with an overhead of 10 times in comparison to a traditional server, paving the way to a near real-time GA-based embedded IDS.
Bioaccumulation of Arsenic, Cadmium, Chromium, Cobalt, Copper, and Zinc in Uroteuthis edulis from the East China Sea
Authors: Li Mengqi , Zhang Baihao , Fang Zhou
Published: July 2024

In this study, the concentrations of trace elements (TEs) in Uroteuthis edulis caught from the East China Sea were determined. There were significant differences between TE concentrations in different body parts. Cu, Zn, and Cd were the most concentrated in the digestive glands and the concentrations of Cr and Co were highest in the gills. No significant differences in concentrations were shown between these tissues. In the four tissues analyzed, the mantle recorded the highest proportion of elemental load, while the digestive glands and gills had the lowest proportions. After maturity, TEs in the mantle showed no significant differences. In the digestive gland, the concentrations of all elements, except Zn, were significantly increased. The gonads illustrated apparent increases in the concentrations of Cr, Cu, and As. In the gills, the concentrations of Co and As were markedly increased.
Variability in Microbial Communities Driven by Particulate Matter on Human Facial Skin
Authors: Fu Kai , Zhou Qixing , Wang Heli
Published: July 2024

Microbial communities are known to play an important role in maintaining ecological balance and can be used as an indicator for assessing environmental pollution. Numerous studies have revealed that air pollution can alter the structure of microbial communities, which may increase health risks. Nevertheless, the relationships between microbial communities and particulate matter (PM) caused by air pollution in terms of health risk assessment are not well understood. This study aimed to validate the influences of PM chemical compositions on microbial communities and assess the associated health risks. Our results, based on similarity analysis, revealed that the stability structure of the microbial communities had a similarity greater than 73%. In addition, the altered richness and diversity of microbial communities were significantly associated with PM chemical compositions. Volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs) exerted a positive influence on microbial communities in different environmental variables. Additionally, a stronger linear correlation was observed between hydroxyl radicals (·OH) and the richness of microbial communities. All estimated health risks from PM chemical compositions, calculated under different environmental variables, significantly exceeded the acceptable level by a factor of more than 49. Cr and 1,2-Dibromoethane displayed dual adverse effects of non-carcinogenic and carcinogenic risks. Overall, the study provides insights into the fundamental mechanisms of the variability in microbial communities driven by PM, which may support the crucial role of PM chemical compositions in the risk of microorganisms in the atmospheric environment.
Student Attendance Patterns as Actionable Early Warning Indicators of High School Graduation Outcomes: Findings from an Urban Alternative Charter School
Authors: Marshall David T.
Published: July 2024

Background: Students who fail to complete high school, on average, have less earnings, pay fewer taxes, and require increased government expenditures compared to those who do reach this milestone. Especially in urban jurisdictions, this can lead to reduced municipal fiscal health. Alternative high schools have been one intervention employed to improve the outcomes of students who have previously dropped out. Also, in recent decades, early warning indicator (EWI) systems have been put in place to flag students who are at risk for not completing school. However, the current EWI metrics for student attendance are insufficient for the population that attends alternative high schools. Methods: Administrative data were obtained from an alternative charter high school in a large urban city in the northeast United States (n = 224). Logistic regression analyses were conducted to test the utility of traditional EWI operationalizations of student attendance against more targeted measures, with student graduation serving as a dichotomous outcome variable. Results: Of the models tested, the model that flagged student non-attendance as missing consecutive days three or more times during the first 12 weeks of school had the greatest explanatory power (McFadden’s R2 = 0.301) and best overall model fit. Conclusions: Traditional definitions of attendance are less useful in schools doing re-engagement work, and more targeted indicators serve as more effective EWIs in these settings.
Tanapox Virus and Yaba Monkey Tumor Virus K3 Orthologs Inhibit Primate Protein Kinase R in a Species-Specific Fashion
Authors: Megawati Dewi , Stroup Jeannine N. , Park Chorong , Clarkson Taylor , Tazi Loubna , Brennan Greg , Rothenburg Stefan
Published: July 2024

Yaba monkey tumor virus (YMTV) and Tanapox virus (TPV) are members of the Yatapoxvirus genus and can infect humans and other primates. Despite the threat posed by yatapoxviruses, the factors determining their host range are poorly understood. In this study, we analyzed the ability of YMTV and TPV orthologs of vaccinia virus K3 (called 012 in YMTV and TPV), which share 75% amino acid identity with one another, to inhibit PKR from 15 different primate species. We first used a luciferase-based reporter, and found that YMTV and TPV K3 orthologs inhibited PKR in a species-specific manner and showed distinct PKR inhibition profiles. TPV 012 inhibited PKR from 11 primates, including humans, substantially better than YMTV 012. In contrast, both K3 orthologs inhibited the other four primate PKRs comparably well. Using YMTV 012 and TPV 012 hybrids, we mapped the region responsible for the differential PKR inhibition to the C- terminus of the K3 orthologs. Next, we generated chimeric vaccinia virus strains to investigate whether TPV K3 and YMTV K3 orthologs could rescue the replication of a vaccinia virus strain that lacks PKR inhibitors K3L and E3L. Virus replication in primate-derived cells generally correlated with the patterns observed in the luciferase-based assay. Together, these observations demonstrate that yatapoxvirus K3 orthologs have distinct PKR inhibition profiles and inhibit PKR in a species-specific manner, which may contribute to the differential susceptibility of primate species to yatapoxvirus infections.
Cross-Neutralizing Anti-Chikungunya and Anti-Dengue 2 IgG Antibodies from Patients and BALB/c Mice against Dengue and Chikungunya Viruses
Authors: Posadas-Mondragón Araceli , Santiago-Cruz José Angel , Pérez-Juárez Angélica , Herrera-González Norma Estela , Sosa-Delgado Sara M. , Wong-Arámbula Claudia Elena , Rodríguez-Maldonado Abril Paulina , Vázquez-Pichardo Mauricio , Duran-Ayala Daniel , Aguilar-Faisal José Leopoldo
Published: July 2024

Dengue (DENV) and Chikungunya (CHIKV) viruses can be transmitted simultaneously by Aedes mosquitoes, and there may be co-infections in humans. However, how the adaptive immune response is modified in the host has yet to be known entirely. In this study, we analyzed the cross-reactivity and neutralizing activity of IgG antibodies against DENV and CHIKV in sera of patients from the Mexican Institute of Social Security in Veracruz, Mexico, collected in 2013 and 2015 and using IgG antibodies of BALB/c mice inoculated with DENV and/or CHIKV. Mice first inoculated with DENV and then with CHIKV produced IgG antibodies that neutralized both viruses. Mice were inoculated with CHIKV, and then with DENV; they had IgG antibodies with more significant anti-CHIKV IgG antibody neutralizing activity. However, the inoculation only with CHIKV resulted in better neutralization of DENV2. In sera obtained from patients in 2013, significant cross-reactivity and low anti-CHIKV IgG antibody neutralizing activity were observed. In CHIKV-positive 2015 sera, the anti-DENV IgG antibody neutralizing activity was high. These results suggest that CHIKV stimulates DENV2-induced memory responses and vice versa. Furthermore, cross-reactivity between the two viruses generated neutralizing antibodies, but exchanging CHIKV for DENV2 generated a better anti-CHIKV neutralizing response.
Diverse Circular DNA Viral Communities in Blood, Oral, and Fecal Samples of Captive Lemurs
Authors: Paietta Elise N. , Kraberger Simona , Lund Michael C. , Vargas Karla L. , Custer Joy M. , Ehmke Erin , Yoder Anne D. , Varsani Arvind
Published: July 2024

Few studies have addressed viral diversity in lemurs despite their unique evolutionary history on the island of Madagascar and high risk of extinction. Further, while a large number of studies on animal viromes focus on fecal samples, understanding viral diversity across multiple sample types and seasons can reveal complex viral community structures within and across species. Groups of captive lemurs at the Duke Lemur Center (Durham, NC, USA), a conservation and research center, provide an opportunity to build foundational knowledge on lemur-associated viromes. We sampled individuals from seven lemur species, i.e., collared lemur (Eulemur collaris), crowned lemur (Eulemur coronatus), blue-eyed black lemur (Eulemur flavifrons), ring-tailed lemur (Lemur catta), Coquerel’s sifaka (Propithecus coquereli), black-and-white ruffed lemur (Varecia variegata variegata), and red ruffed lemur (Varecia rubra), across two lemur families (Lemuridae, Indriidae). Fecal, blood, and saliva samples were collected from Coquerel’s sifaka and black-and-white ruffed lemur individuals across two sampling seasons to diversify virome biogeography and temporal sampling. Using viral metagenomic workflows, the complete genomes of anelloviruses (n = 4), cressdnaviruses (n = 47), caudoviruses (n = 15), inoviruses (n = 34), and microviruses (n = 537) were determined from lemur blood, feces, and saliva. Many virus genomes, especially bacteriophages, identified in this study were present across multiple lemur species. Overall, the work presented here uses a viral metagenomics approach to investigate viral communities inhabiting the blood, oral cavity, and feces of healthy captive lemurs.
Persistence of Antibodies against Measles, Mumps, and Rubella after the Two-Dose MMR Vaccination: A 7-Year Follow-Up Study
Authors: Sarawanangkoor Nasiri , Wanlapakorn Nasamon , Srimuan Donchida , Thatsanathorn Thaksaporn , Thongmee Thanunrat , Poovorawan Yong
Published: July 2024

In 2014, the Expanded Program on Immunization of Thailand changed the timing of the second dose of the measles–mumps–rubella (MMR) vaccine from 4–6 years to 2.5 years, while maintaining the first dose at 9 months of age. This study aimed to examine the dynamics and durability of immune responses induced by the two-dose MMR vaccine in a group of 169 Thai children from 4 to 7 years of age (4.5 years after the second MMR dose). We followed a cohort of healthy children from a clinical trial (ClinicalTrials.gov NCT02408926) where they were administered either the Priorix vaccine (GlaxoSmithKline Biologicals, Rixensart, Belgium) or M-M-RII (Merck & Co., Kenilworth, NJ, USA) at 9 months and 2.5 years of age. Blood samples were collected annually from ages 4 to 7 years. Anti-measles, -mumps, and -rubella IgG levels were evaluated using the enzyme-linked immunosorbent assay (EUROIMMUN, Lubeck, Germany). A total of 169 children completed this study. Over the 4.5 years following the two-dose MMR vaccination, we observed a decline in the seroprotection rates against measles and mumps, but not rubella. Longitudinal monitoring of antibody persistence, among other strategies, will help predict population-level immunity and inform public health interventions to address potential future outbreaks.
Effectiveness of a Counseling Intervention to Increase Vaccination Uptake among Men Who Have Sex with Men during the Mpox Outbreak
Authors: Ogliastro Matilde , Ferrari Allegra , Sticchi Laura , Domnich Alexander , Zappa Giacomo , Di Biagio Antonio , Massaro Elvira , Giribaldi Elisa , Orsi Andrea
Published: July 2024

Men who have sex with men (MSM) are disproportionately impacted by sexually transmitted infections (STIs), including HIV and those preventable through vaccination such as mpox, HPV, HAV, and HBV. A retrospective cohort study was conducted to evaluate the effectiveness of counseling provided during mpox vaccination on the uptake of other recommended vaccines (HPV, HAV, and HBV) and to identify associated factors. Relevant covariates such as nationality, age, HIV status, and use of PrEP were retrieved from electronic medical records. Vaccination status data were retrieved from the regional vaccination registry. Of the 330 participants, 98.8% were males and the mean age was 40.6 years (SD: 11.2). Following consultation, a statistically significant increase for both HPV (from 25.8% to 39.1%) and HAV (from 26.7% to 36.1%) was observed (p < 0.001). The multivariate analysis showed a significant negative association between the uptake of HPV and HBV vaccines and foreign nationality (aOR 0.25 (95%CI 0.08–0.69), p = 0.012; and aOR 0.31 (95%CI 0.11–0.81), p = 0.021). The HBV vaccine uptake was negatively associated with increasing age. Our results suggest that tailored counseling can effectively bridge the gap in vaccine acceptance among vulnerable populations, thereby improving overall public health outcomes.
A Flagellin-Adjuvanted Trivalent Mucosal Vaccine Targeting Key Periodontopathic Bacteria
Authors: Loeurng Vandara , Puth Sao , Hong Seol Hee , Lee Yun Suhk , Radhakrishnan Kamalakannan , Koh Jeong Tae , Kook Joong-Ki , Rhee Joon Haeng , Lee Shee Eun
Published: July 2024

Periodontal disease (PD) is caused by microbial dysbiosis and accompanying adverse inflammatory responses. Due to its high incidence and association with various systemic diseases, disease-modifying treatments that modulate dysbiosis serve as promising therapeutic approaches. In this study, to simulate the pathophysiological situation, we established a “temporary ligature plus oral infection model” that incorporates a temporary silk ligature and oral infection with a cocktail of live Tannerella forsythia (Tf), Pophyromonas gingivalis (Pg), and Fusobacterium nucleatum (Fn) in mice and tested the efficacy of a new trivalent mucosal vaccine. It has been reported that Tf, a red complex pathogen, amplifies periodontitis severity by interacting with periodontopathic bacteria such as Pg and Fn. Here, we developed a recombinant mucosal vaccine targeting a surface-associated protein, BspA, of Tf by genetically combining truncated BspA with built-in adjuvant flagellin (FlaB). To simultaneously induce Tf-, Pg-, and Fn-specific immune responses, it was formulated as a trivalent mucosal vaccine containing Tf-FlaB-tBspA (BtB), Pg-Hgp44-FlaB (HB), and Fn-FlaB-tFomA (BtA). Intranasal immunization with the trivalent mucosal vaccine (BtB + HB + BtA) prevented alveolar bone loss and gingival proinflammatory cytokine production. Vaccinated mice exhibited significant induction of Tf-tBspA-, Pg-Hgp44-, and Fn-tFomA-specific IgG and IgA responses in the serum and saliva, respectively. The anti-sera and anti-saliva efficiently inhibited epithelial cell invasion by Tf and Pg and interfered with biofilm formation by Fn. The flagellin-adjuvanted trivalent mucosal vaccine offers a novel method for modulating dysbiotic bacteria associated with periodontitis. This approach leverages the adjuvant properties of flagellin to enhance the immune response, aiming to restore a balanced microbial environment and improve periodontal health.